Dynamic Effects of Population Aging on Housing Prices in China: Evidence from Prefecture-Level Cities, 2014–2023

Dynamic Effects of Population Aging on Housing Prices in China: Evidence from Prefecture-Level Cities, 2014–2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamic Effects of Population Aging on Housing Prices in China: Evidence from Prefecture-Level Cities, 2014–2023 Yuting Liang, Yike Tu, Hai Zeng, Jinghang Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9125789/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Population aging is emerging as an increasingly important force shaping urban housing markets, yet its impact on housing prices remains theoretically contested and empirically inconclusive. Using an unbalanced panel of approximately 250 prefecture-level cities in China from 2014 to 2023, this study investigates the dynamic effects of population aging on housing prices. To address price persistence, unobserved city heterogeneity, and potential endogeneity between demographic structure and housing markets, we employ dynamic panel models estimated by the system generalized method of moments (System GMM). The results reveal a clear time-varying relationship between population aging and housing prices. Contemporaneous aging is positively associated with both commercial and residential housing prices, suggesting that in the short run, aging may still sustain housing demand through channels such as accumulated household wealth, intergenerational support, and family-based housing arrangements. In contrast, lagged aging exerts a significantly negative effect on housing prices, indicating that deeper demographic aging may gradually weaken medium-term housing demand and slow subsequent housing price growth. Further analysis shows that these effects vary across urban socioeconomic contexts. The negative lagged effect of aging is more pronounced in cities with higher levels of economic development, more advanced industrial structures, and stronger consumption capacity. This suggests that the housing-market consequences of population aging are shaped not only by timing, but also by local structural conditions. This study contributes to the literature by providing city-level evidence on the dynamic and context-dependent effects of population aging on housing prices in China. The findings also offer policy implications for cities confronting both demographic transition and housing market adjustment. Population Aging Housing Prices Dynamic Effects Urban Heterogeneity System GMM Figures Figure 1 Figure 2 1 Introduction Population constitutes the bedrock of socioeconomic development, and as the primary actors in housing transactions, a certain correlation inevitably exists between population and housing prices. In 1998, the urban welfare housing allocation system was entirely supplanted by market-oriented reforms, thereby inaugurating the rapid expansion of the commercial housing market. Over the past two decades, housing prices have experienced a precipitous ascent, characterized by the real estate market’s “high prices, high demand, high supply, and high vacancy rates” (Yu et al., 2016) [1]. This phenomenon has drawn significant scrutiny from both central and local governments, prompting the enactment of policies and regulations to enforce stringent controls. As illustrated in Fig. 1 , both commercial and residential housing prices have experienced rapid increases. In 1998, the average sales prices of commercial and residential housing in China were RMB 2,063 per square meter and RMB 1,854 per square meter respectively. By 2016, these figures had surged to RMB 7,476 per square meter and RMB 7,203 per square meter, reflecting increases of 262.38% and 288.51%. Concurrently, China’s population aging has intensified, with the proportion of the population aged 65 and above standing at a mere 7.43% in 1998. However, by 2016, this proportion had risen to 10.85%, representing a 3.5 percentage point increase, thereby signifying China’s entry into an aging society according to United Nations standards. Drawing upon Maslow’s hierarchy of needs, residential demand constitutes a fundamental safety requirement. The age structure of the population exerts an influence on housing prices through both rigid demand and actual purchasing power. Given that housing prices are a critical issue with profound implications for people’s livelihoods, the impact of population aging on housing prices bears significant ramifications for the stable operation of the socioeconomic system. Consequently, elucidating the intrinsic relationship between these two factors offers a novel perspective for comprehensively understanding national population strategies and examining housing price fluctuations through the lens of population structure. Data Source: The data presented in this study are compiled and derived from the relevant statistical information published in the China Statistical Yearbook across the corresponding years. The relationship between population aging and housing prices has been the subject of extensive scholarly inquiry, with existing research primarily being categorized into four distinct perspectives. The first posits that population aging exerts upward pressure on housing prices. Xu Jianwei et al. (2012), drawing upon a comparative analysis of development experiences in OECD nations, observed that unlike international trends, elderly populations in China exhibit a robust demand for residential housing. This phenomenon is reflected in the concurrent rise of the old-age dependency ratio and housing prices, demonstrating a positive correlation [2]. Further substantiating this claim, Li Tongping et al. (2017) attributed 77% of housing price escalation to inertial expectations, thereby implicating population aging as a salient driver of China's elevated housing prices [3]. Conversely, the second perspective argues that population aging acts as a constraint on housing price increase. For instance, Tan Haiming et al. (2016), employing a long-term Computable General Equilibrium (CGE) model to simulate the medium-to-long-term impact of population aging on housing prices in China, concluded that demographic aging precipitates a stepwise economic deceleration, subsequently triggering a decline in real estate valuations [4]. The third perspective contends that population aging is not a deterministic factor in housing price fluctuations. Ye Yonggang et al. (2016), through the development of a housing price determination model, demonstrated that the impact of the societal elderly dependency burden on housing prices remains indeterminate[5]. Gu Hejun et al. (2017), employing the generalized method of moments (GMM) to estimate the influence of population age structure on housing consumption, found that while age structure partially explains changes in housing area, it fails to account for the sustained rise in Chinese housing prices, suggesting that future rapid increases in demand may no longer underpin housing price growth. The fourth perspective posits a dynamic relationship between aging and housing prices, characterized by spatiotemporal complexity. Influenced by specific historical policies and the ‘altruistic motives’ of the Chinese elderly population, the policy-induced housing wealth generated by China’s housing reform policies has been gradually released through inter-generational family transfers. This in turn has facilitated the combined release of dual-generation savings into the real estate market, thereby propelling rapid urban housing price appreciation. However, as the dividends of housing reform diminish, the economic pressures of elderly care in an aging society are anticipated to reverse the housing supply-demand equilibrium, subjecting housing prices to prolonged downward pressure (Chen Guojin et al., 2013; Hu Mingzhi et al., 2017) [6–7]. In summary, the relationship between population aging and housing prices remains inconclusive. Within the unique context of China’s traditional filial culture and socioeconomic landscape, the impact of population aging on housing prices warrants further in-depth investigation. In contrast to prior investigations, this study constructs a panel data model, matching housing price data with population aging data at the prefecture-level city scale. Utilizing methodologies such as the system generalized method of moments (GMM), it provides an empirical examination of the relationship between population aging and housing prices, as well as the underlying mechanisms. The marginal contributions of this research are primarily manifested in the following aspects: (1) Enhanced granularity of analysis. Due to data acquisition limitations, previous studies have predominantly utilized provinces as the unit of analysis. However, given the pronounced socio-economic sensitivity of housing prices and the substantial intra-provincial heterogeneity, employing provinces as the unit of analysis may compromise the granularity and accuracy of research findings. This study, drawing upon panel data from 250 prefecture-level cities nationwide, addresses the limitations of macro-level analyses that overlook regional internal heterogeneity, thereby offering a more nuanced and empirically grounded representation of the Chinese context. (2) Systematization of research. Previous studies have often explored the issue from isolated perspectives, such as industrial structure, purchasing power, or urbanization. This study, conversely, undertakes a systematic analysis of the multi-factorial influences of population aging on housing price fluctuations, endeavoring to situate their relationship within a unified socio-economic framework for a comprehensive investigation. (3) Deepening the depth of research. The extant literature on this subject remains relatively sparse and has yet to yield a consensus. While many studies have focused on the impact of aging on housing prices, there is a notable dearth of attention paid to the mechanisms through which population aging influences housing price dynamics. The remainder of the paper is organized as follows: Section Two presents the research hypotheses based on theoretical insights and a literature review; Section Three outlines the econometric model, variable indicators, and data sources; Section Four reports the empirical findings; Section Five examines how the effect of population aging on housing prices varies across different socioeconomic contexts; and finally, the concluding section provides a summary of the findings and a discussion of their implications. 2 Theoretical Analysis and Research Hypothesis Real estate, as a pivotal variable influencing national economies, has consistently remained a focal point of research both domestically and internationally. Existing studies have elucidated the determinants of housing price fluctuations from various perspectives, including land tenure systems, monetary interest rates, and speculative bubbles (Zhang Tao et al., 2008; Zhou Hui et al., 2009; Kuang Weida, 2010) [8–10]. However, the explanation of housing price fluctuations cannot be confined to the interpretation of macroeconomic structures and social policies. With the escalating severity of population aging, the role of demographic factors in housing price dynamics is increasingly being recognized. The life-cycle theory elucidates the mechanism behind housing price fluctuations lies in the interplay between the population’s age structure and economic activity. To maximize lifetime utility, individuals smooth consumption and savings across different age stages, with a propensity for savings and investment during middle age, and a higher likelihood of consumption, borrowing, and asset liquidation in youth and old age (Modigliani, 1966 ) [11]. Extrapolating to the societal level, a predominance of middle-aged individuals generates excess demand in the housing market, thereby inflating housing prices. Conversely, an elevated proportion of elderly individuals reverses the housing market equilibrium of supply and demand, leading to an oversupply of housing assets and subsequent price depreciation. Bakshi et al. ( 1994 ), employing variations in the coefficient of risk aversion, arrived at similar conclusions, positing that while investors initially favor real estate investment, their pursuit of risk premium levels increases with age, resulting in a shift of wealth towards financial assets, consequently driving down housing prices and elevating stock prices [12]. Related “asset meltdown hypotheses” also posit that the elderly population’s tendency to divest assets precipitates housing price declines, potentially culminating in real estate market collapse (Poterba et al., 1991 ) [13]. Mankiw et al. (1989), employing a dynamic asset demand-supply model (the Mankiw-Well model) to estimate housing demand parameters based on age structure, observed a ‘hump-shaped’ pattern in individual life-cycle housing demand, characterized by a low demand level prior to age 20, a significant surge to peak demand between ages 20 and 30, and a subsequent decline at a rate of 1% per annum after age 40 [14]. Subsequent scholarly investigations have subjected the M-W model to extensive scrutiny. Ermisch ( 1996 ) demonstrated a significant relationship between the UK population structure and residential demand growth, indicating that population aging would attenuate the rate of increase in residential housing demand [15]. Ohtake et al. (1996), examining interplay between Japan’s demographic structure and the temporal dynamics of the real estate market, revealing that short-term population fluctuations substantially affect housing price fluctuations [16]. Furthermore, studies utilizing overlapping generations models (OLG) have revealed that the aging of the post-World War II “baby boom" generation contributed to asset price declines and a reduction in capital returns in the United States (Abel, 2001 ; Krueger et al., 2006) [17–18]. Analogous findings have been reported in studies conducted in regions such as Ireland (Martin, 2005 ) [19]. As evidenced by the aforementioned literature, a complex relationship exists between population aging and housing prices. Within the contemporary Chinese context, four decades of reform and opening-up have resulted in the accumulation of wealth among a segment of the population, which is now predominantly middle-aged to elderly. Given the appreciation of housing prices, this demographic group is inclined to utilize real estate as a means of asset preservation and appreciation, thereby exerting upward influence on housing prices. However, with the progressive intensification of societal aging, the economic burden on the working-age population increases, potentially leading to a decline in optimistic expectations regarding future housing prices. This in turn may manifest as a decrease in anticipated housing prices within the real estate market. Consequently, we propose the following hypothesis H1: H1: Population aging has time-varying effects on housing prices: it may raise current housing prices in the short run but depress subsequent housing price growth over time. The existing literature does not offer a unanimous view regarding the magnitude and mechanisms through which population aging influences housing prices. Existing research has primarily identified the following mechanisms: Firstly, the inherent characteristics of real estate, including land scarcity, high investment returns, and universal housing demand, endow housing consumption with dual attributes of both consumption and investment (Carliner, 1973 ) [20]. Consequently, real estate prices are fundamentally anchored in macroeconomic development levels (Shen Yue et al., 2002) [21], with China’s rapid economic growth exerting a decisive influence on housing price appreciation (Lin Yifu et al., 2003) [22]. The sustained economic growth in China has stimulated urban housing demand, thereby propelling housing price escalation (Chen et al., 2011 ) [23]. Conversely, population aging exerts a significant negative impact on economic growth through its effects on labor supply (Zhu Yuepu et al., 2017) [24], human capital (Zhang Xiuwu et al., 2018 ) [25], and material capital investment (Song Qi et al., 2017) [26]. This suggests that the level of regional economic development may condition the magnitude of the effect of population aging on housing prices. Consequently, we propose the following hypothesis H2a: H2a: The effect of population aging on housing prices is moderated by the level of regional economic development. Secondly, the advancement and rationalization of industrial structure exert a positive influence on urban housing prices (Fan et al., 2011; Fan et al., 2018) [27–28]. Malpezzi ( 2002 ), using the United States as a case study, found that a 1% increase in industrial agglomeration correlates with a 28% rise in real estate prices [29]. However, research by Roger and Wasmer (2012) suggests that population aging diminishes labor force adaptability [30], thereby constraining industrial agglomeration. The aging of the labor force also indirectly impedes the upgrading of industrial structures (Yang Xue et al., 2011) [31]. The exacerbation of societal aging hampers the optimization and upgrading of industrial structures through labor productivity and elderly dependency burden effects (Wang Wei et al., 2015) [32]. Drawing upon the aforementioned research, it is evident that population aging exerts a negative impact on industrial structure, while industrial structure positively influences housing prices. Consequently, we propose the following hypothesis: H2b: H2b: The effect of population aging on housing prices is moderated by local industrial structure. Finally, it is the economic attributes of the population, rather than its natural attributes, that influence housing prices (Wang Sheng et al., 2017) [33]. The relationship between housing prices and population age structure is also contingent upon disparities in actual consumption capacity. From the perspective of inter-generational savings transfer, parental financial assistance constitutes a substantial proportion of housing purchases arising from declining household sizes (Ren Wei et al., 2016) [34]. Parental subsidies for children’s housing acquisition result in a reduction of elderly individuals’ personal assets and a decline in their actual consumption capacity. This phenomenon is particularly pronounced in eastern regions, where the contradiction between youth low-consumption capacity and high housing prices is acute. Furthermore, from the perspective of societal demographic transition, when societal aging progresses into a demographic debt phase, society may transition into a ‘low-desire’ state characterized by decreased fertility, delayed marriage, and reduced home-ownership, leading to diminished consumption levels and downward pressure on housing prices (Li Tongping et al., 2017) [3]. Population aging can lead to a decline in regional consumption levels, which in turn directly affects regional housing prices. Consequently, we propose the following hypothesis: H2c: H2c: The effect of population aging on housing prices is moderated by regional consumption capacity. 3 Research Design 3.1 Econometric Model Considering the issue of endogeneity, this study employs the system generalized method of moments (GMM) to test the hypotheses outlined above. The specific model is as follows: 3.2 Variable Selection Dependent Variables: Housing prices ( hp ), specifically commercial housing prices ( hp1 ) and residential housing prices ( hp2 ). Both commercial and residential housing prices are calculated by dividing the total sales revenue by the total sales area. Commercial housing prices are used for the baseline regression, while residential housing prices are employed for robustness checks. Prior to model regression, nominal housing price data are deflated using the Consumer Price Index (CPI) to obtain real housing price values. Core Independent Variable: Population aging ( age ). Following the United Nations’ criteria for an aging society and commonly used metrics in academic literature, key indicators for measuring regional population aging include the proportion of the population aged 65 and above, old-age dependency ratio, and median age (Lin Lin, 2007 ) [35]. This study utilizes the proportion of the population aged 60 and above ( age60 ) and the proportion of the population aged 65 and above ( age65 ) to represent the level of regional population aging. In addition to the core independent variable, control variables are selected based on relevant literature and the identified determinants of housing prices. These variables are categorized into macroeconomic environment, population socio-economic attributes, and regional public resource allocation. (1) From the angle of macroeconomic environment, while population aging may reduce overall housing demand, economic growth itself stimulates housing acquisition demand (Farkas, 2011 ) [36]. Therefore, this study employs variables such as per capita GDP (ln pgdp ), the proportion of secondary industry ( sepconsumd ), the proportion of tertiary industry ( third ), total bank loans ( bankloan ), and the proportion of real estate investment in total investment ( hou_inv ) to characterize the regional macroeconomic environment. Generally, higher levels of regional economic development and monetary easing stimulate social housing demand, leading to housing price appreciation (Martin, 2005 ) [19]. (2) From the perspective of population socio-economic attributes: This study employs indicators such as population density (ln popren ), average wage (ln wage ), and per capita consumption expenditure (ln pconsum ) to characterize the socio-economic attributes of the population. Due to land scarcity, the growth rate of housing stock cannot keep pace with population growth. The limited housing capacity per unit area results in housing price escalation with increasing population density. Furthermore, the environmental pressures associated with population growth contribute to higher real estate development complexity and costs, thereby driving up housing prices (Wu Lichao et al., 2018) [37]. From an economic investment and expectation perspective, the high asset preservation and inflation-hedging properties of real estate render it a valuable tool for household wealth preservation and appreciation (Gao Bo et al., 2013) [38]. Consequently, indicators of individual economic well-being, such as wages and consumption expenditure, warrant consideration. (3) From the perspective of regional public resource allocation, within the framework of housing economics theory, housing demand exhibits hierarchical characteristics. Beyond basic infrastructure needs such as shelter, demand arises for location environment amenities, including favorable employment opportunities and high-quality public services (Rosen, 2002 ) [39]. Housing prices reflect a composite of structural and location environment’s attributes, with a higher provision of quality public goods increasing consumers’ willingness to pay, thereby driving up housing prices. To this end, this study employs the number of university students per 10,000 residents (ln stu ), the volume of library books per 10,000 residents (ln lib ), and the number of hospital beds per 10,000 residents (ln med ) as indicators of regional education, cultural, and medical resource allocation. These variables serve to quantify the impact of regional public resource allocation differentials on housing prices. 3.3 Data Sources Housing price data are mainly drawn from the China City Statistical Yearbook, which provides city-level information on housing sales revenue and sales area. Population aging indicators are constructed from prefecture-level population age-structure statistics published by the National Bureau of Statistics of China. The remaining control variables are obtained from the China City Statistical Yearbook and the China Urban Statistical Yearbook for the corresponding years. After matching the housing-price data with the population-aging indicators and excluding cities with substantial missing observations, we obtain an unbalanced panel of approximately 250 prefecture-level cities covering the period 2014–2023. To reduce the influence of extreme values, key continuous variables are winsorized at the 1st and 99th percentiles. Table 1 reports the descriptive statistics of the main variables. According to the international criteria for an aging society—defined as a society in which the proportion of the population aged 60 and above exceeds 10%, or the proportion aged 65 and above exceeds 7%—China had fully entered an aging society during the sample period. Specifically, during 2014–2023, the average proportions of the population aged 60 and above and 65 and above reached 21.1% and 15.4%, respectively, indicating a deepening stage of population aging. In cities experiencing the most severe population aging, the proportion of elderly residents exceeded 24% for the population aged 60 and above and 17% for those aged 65 and above, underscoring the growing demographic pressure associated with rapid aging. Moreover, housing prices exhibit substantial regional heterogeneity across cities. The logarithm of real housing prices ranges from approximately 5.99 to 10.90, reflecting pronounced disparities in regional housing market conditions. Table 1 Descriptive Statistics Variables Definition Sample Size Mean Standard Deviation Minimum Maximum Dependent Variables ln hp1 Natural logarithm of commercial housing prices 2,250 8.35 0.65 6.10 10.90 ln hp2 Natural logarithm of residential housing prices 2,250 8.28 0.62 5.99 10.70 Independent Variables age60 Proportion of the population aged 60 and above (%) 2,250 21.1 3.5 11.0 24.8 age65 Proportion of the population aged 65 and above (%) 2,250 15.4 2.8 6.5 17.6 Control Variables ln pgdp Natural logarithm of GDP per capita 2,250 11.05 0.90 8.90 13.50 second Proportion of secondary industry (%) 2,250 0.34 0.07 0.12 0.62 third Proportion of tertiary industry (%) 2,250 52.0 9.51 25.02 84.67 bankloan Natural logarithm of total bank loans 2,250 1.45 0.60 0.30 3.22 hou_inv Proportion of real estate investment in total investment 2,250 0.18 0.08 0.03 0.45 ln popren Natural logarithm of population density 2,250 6.60 0.85 3.12 9.49 ln wage Natural logarithm of average wage 2,250 11.10 0.35 9.61 12.51 ln pconsum Natural logarithm of per capita consumption expenditure 2,250 10.25 0.63 8.51 12.28 ln stu Natural logarithm of the number of university students per 10,000 people 2,250 6.19 0.81 2.48 9.01 ln lib Natural logarithm of the number of books per 10,000 people 2,250 4.56 0.91 1.22 8.23 ln med Natural logarithm of the number of hospital beds per 10,000 people 2,250 4.31 0.55 2.12 6.21 4 Results Analysis 4.1 Baseline Regression Based on Eq. ( 1 ), we estimate the model using the System GMM approach, and Table 2 presents the baseline regression results. Among the six sets of estimates reported in Table 2 , the first four models employ the proportion of the population aged 60 and above as well as that of individuals aged 65 and above—both without and with additional control variables—for the estimation of commercial housing prices. For these four models, the P-values for the AR(2) tests are not statistically significant, and the Sargan test P-values are also insignificant, which indicates that the System GMM estimates are reliable. Moreover, the Wald Chi2 test results further confirm the validity of the estimations for these four models. Notably, the one-period lagged housing price variable is non-significant in these four models, suggesting a limited degree of path dependency in housing prices. The relationship between the proportion of the population aged 60 and above and commercial housing prices is examined in column (1). The results indicate that population aging has a significantly positive effect at the 1% level. This indicates that population aging exerts a positive influence on commercial housing prices; specifically, a 1% increase in the proportion of the population aged 60 and above is associated with a 1.9% increase in housing prices. Upon the inclusion of other control variables in column (2), the significance of the population aging variable remains at the 1% level, and the coefficient exhibits no substantial change, suggesting a robust effect of population aging on commercial housing prices. Columns (3) and (4) present estimates using the proportion of the population aged 65 and above as the core independent variable. Column (3) demonstrates a statistically significant positive relationship between the proportion of the population aged 65 and above and commercial housing prices. Following the inclusion of control variables in column (4), the coefficient remains statistically significant and positive, with a value of 0.018, indicating that a 1% increase in the proportion of the population aged 65 and above is associated with a 1.8% increase in housing prices. Given the findings of Li Tongping et al. (2017), which suggest that anticipated population aging exerts a suppressive effect on housing price appreciation [3], we examined the impact of one-period lagged population aging on current housing prices. This analysis aims to understand the influence of current population aging on future housing prices—in other words, its effect on housing prices. Columns (5) and (6) present the corresponding estimation results. Both lagged aging indicators exhibit a significant impact on commercial housing prices, albeit with a reversal in the direction of influence. Specifically, population aging demonstrates a negative relationship with housing prices, indicating that a 1% increase in the proportion of the population aged 60 and above is associated with a 0.9% decrease in anticipated housing prices. Similarly, a 1% increase in the proportion of the population aged 65 and above is associated with a 1.5% decrease in anticipated housing prices. This reversal in coefficient sign suggests that the impact of population aging on housing prices in China is not unidirectional. The phenomenon of housing prices exhibiting an ‘initial increase followed by a subsequent decrease’ in relation to population aging has also been corroborated by international scholars. Eichholtz and Lindenthal ( 2014 ), in their study of the United Kingdom, found that the suppressive effect of population aging on housing demand is conditional, with a notable decline in demand occurring primarily towards the terminal phase of elderly individuals’ life cycles [40]. However, the impact of population aging on housing price fluctuations in China is characterized by greater complexity and is deeply intertwined with the nation’s unique historical and socio-economic context. The demographic growth patterns associated with an aging society exhibit differentiation. During the incipient stages of aging, the overall population continues to experience growth, albeit at a decelerating rate. It is only in the advanced stages of aging that a stationary population, or even population decline, becomes evident (Li Tongping et al., 2017) [3]. From the perspective of familial structural evolution, the principle of ‘one household requiring one residence’ implies that population aging and the nucleation of families have generated a substantial number of elderly individuals living alone, thereby augmenting housing demand. However, the increased dependency of aging populations on their offspring may lead to a higher probability of cohabitation. This dynamic, as reflected in the real estate market, suggests that housing demand declines only when elderly individuals return to reside with their children, resulting in a reduction in the number of households. Consequently, scholars have posited an ‘inverted U-shaped’ relationship between population aging and housing demand, wherein the stimulative effect of aging on housing demand is positive during the incipient stages, but reverses direction upon reaching a critical threshold (Ding Yang et al., 2018) [41]. Furthermore, considering the broader socio-political policy context, the demographic age structure transition in China coincided with the implementation of housing policy reforms. The historical wealth generated by the large-scale privatization of public housing during the housing reform period has been subject to long-term inter-generational transfers. The concurrent implementation of the one-child policy, which resulted in a reduction in the number of offspring, further amplified the inter-generational transfer effect of welfare housing, leading to a pronounced prevalence of elderly individuals providing financial assistance for housing purchases (Hu Mingzhi, 2017) [7]. It is posited that the suppressive effect of population aging on housing prices will become prominent only upon the complete dissipation of the housing reform dividends. Consequently, hypothesis H1 is substantiated, indicating that the current demographic aging in China exerts an upward influence on housing prices. However, with the progressive intensification of aging and the ongoing adjustments associated with China’s economic entering a new normal, the inhibitory influence of population aging on housing prices is expected to become increasingly pronounced. Table 2 Baseline regression results (1) (2) (3) (4) (5) (6) ln hp1 ln hp1 ln hp1 ln hp1 ln hp1 ln hp1 L.ln hp1 -0.002 -0.009 -0.002 -0.008 -0.015 ** -0.014 * (0.006) (0.007) (0.006) (0.008) (0.007) (0.008) age60 0.019 *** 0.013 *** (0.003) (0.004) age65 0.028 *** 0.018 *** (0.004) (0.006) L. age60 -0.009 *** (0.003) L. age65 -0.015 *** (0.005) ln pgdp 0.009 *** 0.009 *** 0.008 *** 0.008 *** (0.002) (0.002) (0.002) (0.002) second 0.533 0.537 0.317 0.270 (0.345) (0.352) (0.363) (0.374) third -0.005 *** -0.005 *** -0.006 *** -0.006 *** (0.002) (0.002) (0.002) (0.002) bankloan -0.004 -0.007 -0.006 -0.009 (0.013) (0.013) (0.012) (0.013) hou_inv 0.185 ** 0.185 ** 0.204 ** 0.199 ** (0.087) (0.088) (0.088) (0.089) ln popden 0.009 0.006 0.015 0.015 (0.026) (0.026) (0.025) (0.025) ln wage 0.014 0.013 0.041 0.033 (0.058) (0.058) (0.059) (0.058) ln pconsum 0.033 0.041 0.042 0.054 (0.047) (0.047) (0.049) (0.049) ln stu 0.003 0.002 0.005 0.007 (0.010) (0.010) (0.010) (0.010) ln lib -0.008 -0.008 -0.008 -0.007 (0.013) (0.013) (0.014) (0.014) ln med 0.092 *** 0.088 *** 0.096 *** 0.090 *** (0.032) (0.032) (0.032) (0.033) Constant Terms 8.153 *** 6.656 *** 8.171 *** 6.635 *** 6.642 *** 6.645 *** (0.070) (0.568) (0.070) (0.570) (0.571) (0.573) N 2250 2250 2250 2250 2250 2250 AR(2) P-value 0.1808 0.1765 0.1438 0.1808 0.198 0.198 Sargen P-value 0.4367 0.3973 0.3720 0.4174 0.227 0.351 Regional Effects Y Y Y Y Y Y Time Effects Y Y Y Y Y Y Wald Chi2 9226.968 7335.188 9146.669 7354.366 7063.034 6944.328 Note: Standard errors are presented in parentheses. Values within square brackets represent p-values for the F-test, * p < 0.1 ** p < 0.05 *** p < 0.01, this convention applies throughout the document. Regarding other control variables: (1) In terms of the macroeconomic environment, per capita GDP exerts a positive influence on housing prices, indicating that regional economic development drives housing price appreciation. Specifically, for a 1% increase in per capita GDP, housing prices rise by 0.8% to 0.9%. The influence of various industries on housing prices differs. The relationship between the proportion of secondary industry and housing prices does not achieve statistical significance. Conversely, the proportion of tertiary industry demonstrates a significant negative relationship with housing prices at the 1% level, suggesting that a 1% increase in the proportion of tertiary industry is associated with a 0.5% to 0.6% decrease in housing prices. The correlation between the proportion of real estate investment in total investment and housing prices is statistically significant at the 5% level, with a coefficient of 0.185, indicating that a 1% increase in the proportion of real estate investment leads to a substantial 18.5% increase in housing prices. This phenomenon may be attributed to the fact that rapid housing price appreciation implies higher returns on real estate investment. The allure of profitability and expectations of further price escalation incentivize households to increase real estate investments, thereby contributing to the elevation of housing prices. Upon the inclusion of the lagged aging variable, the coefficient increases to 20.4%. (2) Regarding population socio-economic attributes, population density, average wage, and per capita consumption expenditure do not achieve statistical significance. However, the coefficients for all three variables exhibit a positive relationship with housing prices. (3) Concerning regional public resource allocation, the relationship between the number of hospital beds per 10,000 residents and housing prices is statistically significant at the 1% level. A 1% increase in the number of hospital beds is associated with a 0.88% to 0.92% increase in housing prices. This suggests that public resources are capitalized and integrated into housing price valuation, generating a premium effect in the real estate market. The magnitude of this premium is contingent upon the uneven distribution of public goods across cities, with housing prices in prime locations typically being higher, hence the positive coefficient. Figure 2 illustrates the estimated marginal effects of population aging on commercial housing prices, distinguishing between contemporaneous and one-period lagged effects. The points represent coefficient estimates derived from the System GMM baseline regressions, and the vertical bars denote 95% confidence intervals. The results indicate that population aging exerts a positive contemporaneous effect on housing prices, while its lagged effect is significantly negative, highlighting pronounced time heterogeneity in the impact of aging on housing markets. 4.2 Robustness Check Using Residential Housing Prices To assess the robustness of the baseline results, Table 3 reports a set of alternative estimations in which residential housing prices (lnhp2) are employed as a substitute for commercial housing prices (lnhp1). Compared with commercial housing prices, residential housing prices are more closely related to actual housing demand and therefore provide a more demand-oriented perspective for validating the empirical findings. The results indicate that the core conclusions remain largely unchanged. Consistent with the baseline estimations, current population aging continues to exert a positive effect on residential housing prices, while one-period lagged population aging exhibits a significant negative effect, confirming the coexistence of contemporaneous stimulative effects and lagged suppressive effects of population aging on housing prices. This finding suggests that the impact of aging on housing prices is robust across different housing market segments. Specifically, column (1) examines the effect of the proportion of the population aged 60 and above on residential housing prices. The coefficient remains positive and statistically significant at the 10% level, indicating that a 1% increase in the elderly population share is associated with a 0.7% increase in residential housing prices. Although the magnitude of the coefficient is smaller than that in the baseline regression, the direction and significance remain consistent, implying that population aging continues to support housing prices, albeit with a weaker effect in demand-oriented residential markets. Column (2) reports the estimation results using the proportion of the population aged 65 and above as the key explanatory variable. The coefficient is positive and significant at the 10% level, suggesting that a 1% increase in the population aged 65 and above raises residential housing prices by approximately 1.1%. Columns (3) and (4) further investigate the impact of population aging on housing prices by introducing one-period lagged aging variables. The results show that lagged aging exerts a statistically significant negative effect on residential housing prices. A 1% increase in the lagged proportion of the population aged 60 and above and 65 and above leads to a 0.2% and 0.4% decrease in current residential housing prices, respectively. These findings reinforce the baseline conclusion that while population aging supports housing prices in the short term, it generates downward pressure on future housing prices by weakening long-term demand expectations. In addition, the one-period lagged residential housing price exhibits a significant negative coefficient, suggesting that residential housing prices display weaker price persistence and are more responsive to changes in market fundamentals. Compared with the baseline regression, the statistical significance of several control variables is enhanced. In particular, the proportion of secondary industry and average wage demonstrate significantly positive effects at the 1% level, while library book holdings per 10,000 residents exhibit a negative effect at the 10% level. Moreover, the coefficient of the proportion of real estate investment in total investment increases substantially, ranging from 13.9% to 17.5%, indicating that residential housing prices are more sensitive to socio-economic conditions and investment dynamics. The comparison between Table 2 and Table 3 shows that replacing commercial housing prices with residential housing prices does not alter the main empirical conclusions. Instead, it further confirms the robustness of the estimated effects and highlights that the influence of population aging is more pronounced in investment-driven housing markets, while residential housing prices respond more strongly to underlying economic and social fundamentals. Table 3 Robustness checks using residential housing prices (1) (2) (3) (4) ln hp2 ln hp2 ln hp2 ln hp2 L.ln hp2 -0.017 ** -0.016 ** -0.019 *** -0.020 *** (0.007) (0.007) (0.007) (0.007) age60 0.007 * (0.004) age65 0.011 * (0.006) L. age60 -0.002 ** (0.001) L. age65 -0.004 *** (0.001) ln pgdp 0.010 *** 0.010 *** 0.011 *** 0.011 *** (0.002) (0.002) (0.002) (0.002) second 1.171 *** 1.228 *** 1.225 *** 1.258 *** (0.335) (0.340) (0.323) (0.325) third -0.004 ** -0.005 ** -0.005 *** -0.005 *** (0.002) (0.002) (0.002) (0.002) bankloan -0.017 -0.016 -0.010 -0.011 (0.012) (0.012) (0.011) (0.011) hou_inv 0.360 *** 0.356 *** 0.343 *** 0.357 *** (0.092) (0.093) (0.090) (0.090) ln popden -0.006 -0.007 -0.013 -0.014 (0.023) (0.023) (0.023) (0.023) ln wage 0.144 *** 0.143 *** 0.166 *** 0.163 *** (0.053) (0.053) (0.056) (0.056) ln pconsum -0.011 -0.008 -0.020 -0.022 (0.036) (0.036) (0.037) (0.037) ln stu 0.007 0.007 0.006 0.007 (0.010) (0.010) (0.010) (0.010) ln lib -0.022 * -0.022 * -0.024 ** -0.024 ** (0.011) (0.011) (0.012) (0.012) ln med 0.138 *** 0.139 *** 0.143 *** 0.142 *** (0.033) (0.033) (0.032) (0.033) Constant Terms 5.438 *** 5.392 *** 5.437 *** 5.489 *** (0.514) (0.514) (0.545) (0.552) N 2250 2250 2250 2250 AR(2) P-value 0.3467 0.3345 0.327 0.318 Sargen P-value 0.3993 0.4604 0.479 0.407 Regional Effects Y Y Y Y Time Effects Y Y Y Y 5 Heterogeneity Analysis: Moderating Effects of Socioeconomic Context Significant regional development imbalances exist within China, manifested not only in economic development environments but also in the socio-economic attributes of its population. Consequently, the following question arises: Does the impact of population aging on housing prices vary across different socio-economic contexts? Population aging may interact with regional economic development, thereby influencing housing price fluctuations. To address this question, this section examines an extension of the primary research proposition by incorporating interaction items into the original regression equation. The results of this analysis are presented in Table 4 . Table 4 displays the regression results obtained by including interaction items between population aging indicators and per capita GDP, industrial structure, and consumption levels as explanatory variables. The findings indicate that, regardless of whether population aging is measured using the 60-year-old or 65-year-old criteria, the coefficients of the interaction items are consistently negative and statistically significant at the 5% level or higher. Consequently, the aforementioned regression results indicate a significant negative correlation between population aging and socio-economic development. Specifically, under the condition of controlling for other variables, the higher the level of socio-economic development in a region, the stronger the inhibitory effect of population aging on housing price appreciation. In column (1), while the coefficients for both age60 and ln pgdp are positive, the estimated coefficient for age60*ln pgdp is negative and statistically significant at the 5% level. This result suggests that although both population aging and economic development exert a positive influence on housing prices, their interaction items demonstrates a negative influence. This is attributed to the negative impact of population aging on economic development levels, thereby suggesting that the dampening effect of aging on housing prices is stronger in more economically developed regions, consistent with hypothesis H2a. Regarding the interaction items between population aging and industrial structure, the regression results in columns (2) and (3) consistently demonstrate a negative estimated coefficient, which is statistically significant at the 1% level for both. These results validate the potential interaction between population aging and industrial structure, as well as its inhibitory effect on housing price appreciation, which aligns with the arguments presented in hypothesis H2b. Column (4) examines the relationship between housing prices and the interaction items of population aging and consumption levels. The results indicate that the coefficient is statistically significant at the 1% level and negative, suggesting that consumption levels moderate the relationship between population aging and housing prices. Specifically, in regions with higher levels of population aging, the positive correlation between aging and housing price appreciation, as mediated by the consumption interaction items, is suppressed. This provides empirical support for hypothesis H2c. In this context, the estimation results of this regression not only align with the findings of existing literature but also contribute to a more nuanced understanding of the complex interplay between population aging, socio-economic indicators, and housing price fluctuations. Table 4 Moderating effects: commercial housing prices (1) (2) (3) (4) (5) (6) (7) (8) ln hp1 ln hp1 ln hp1 ln hp1 ln hp1 ln hp1 ln hp1 ln hp1 L.ln hp1 -0.010 -0.012 -0.008 -0.012 -0.009 -0.011 -0.008 -0.012 (0.007) (0.007) (0.008) (0.008) (0.008) (0.007) (0.008) (0.008) age60 0.072 *** 0.082 *** 0.069 *** 0.255 *** (0.025) (0.014) (0.016) (0.049) age60* ln pgdp -0.006 ** (0.002) age60*second -0.195 *** (0.035) age60*third -0.001 *** (0.000) age60* ln pconsum -0.025 *** (0.005) age65 0.072 ** 0.080 *** 0.059 *** 0.286 *** (0.029) (0.020) (0.023) (0.064) age65* ln pgdp -0.005 ** (0.003) age65*second -0.177 *** (0.051) age65*third -0.001 * (0.001) age65* ln pconsum -0.028 *** (0.007) Constant Terms 5.970 *** 6.163 *** 6.281 *** 4.096 *** 6.250 *** 6.498 *** 6.505 *** 4.844 *** (0.624) (0.585) (0.580) (0.763) (0.591) (0.568) (0.573) (0.692) N 2250 2250 2250 2250 2250 2250 2250 2250 AR(2) P-value 0.1884 0.1361 0.1412 0.1950 0.1762 0.1834 0.1678 0.1987 Sargen P-value 0.3993 0.3041 0.3434 0.4573 0.1875 0.1503 0.1557 0.1999 Regional Effects Y Y Y Y Y Y Y Y Time Effects Y Y Y Y Y Y Y Y Wald Chi2 7502.4 7559.7 7443.5 7965.8 7842.3 7642.1 7532.9 7491.5 Furthermore, to further ascertain the reliability of the regression estimation results, this section employs residential housing prices as an alternative measure of the dependent variable, housing prices, to conduct additional robustness analyses. The resulting findings are presented below. Table 5 Moderating effects: residential housing prices (1) (2) (3) (4) (5) (6) (7) (8) ln hp2 ln hp2 ln hp2 ln hp2 ln hp2 ln hp2 ln hp2 ln hp2 L.ln hp2 -0.019 *** -0.015 ** -0.016 ** -0.019 *** -0.018 *** -0.016 ** -0.016 ** -0.019 *** (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) age60 0.095 *** 0.077 *** 0.066 *** 0.296 *** (0.026) (0.014) (0.013) (0.048) age60* ln pgdp -0.008 *** (0.002) age60*second -0.190 *** (0.033) age60*third -0.001 *** (0.000) age60* ln pconsum -0.030 *** (0.005) age65 0.089 *** 0.061 *** 0.070 *** 0.358 *** (0.031) (0.019) (0.019) (0.065) age65* ln pgdp -0.007 *** (0.003) age65*second -0.001 *** (0.000) age65*third -0.168 *** (0.047) age65* ln pconsum -0.036 *** (0.007) Constant Terms 4.462 *** 4.683 *** 4.798 *** 2.210 *** 4.762 *** 4.992 *** 4.903 *** 2.687 *** (0.571) (0.557) (0.532) (0.769) (0.577) (0.542) (0.565) (0.765) N 2250 2250 2250 2250 2250 2250 2250 2250 AR(2) P-value 0.1946 0.7667 0.5439 0.5075 0.2368 0.4174 0.4854 0.5119 Sargen P-value 0.2707 0.9367 0.7091 0.6737 0.3118 0.5063 0.6761 0.6254 Regional Effects Y Y Y Y Y Y Y Y Time Effects Y Y Y Y Y Y Y Y Wald Chi2 9272.621 9925.487 10011.71 10237.38 9554.61 10146.73 10127.09 10145.68 Table 5 presents regression results consistent with those in Table 4 . Columns (1) to (4) display regression results where the interaction items including the percentage of the population aged 60 and above and per capita GDP, industrial structure, and consumption level are used as primary explanatory variables, with other control variables included. Columns (5) to (8) replace the population aging variable with the percentage of the population aged 65 and above for interaction item analysis. Even after employing alternative indicators, the impact of population aging on housing prices remains significant at the 1% level, with coefficients showing minimal variation. The interaction items consistently exhibit significant negative coefficients at the 1% level, reinforcing the robustness of the regression results. 6 Summary and Discussion Population aging has become one of the most profound structural challenges confronting China’s long-term socio-economic sustainability. Housing prices, as a central indicator of macroeconomic performance and household wealth accumulation, constitute a critical linkage between demographic dynamics and economic activity (Zou Jin et al., 2015). Using panel data from 250 prefecture-level cities, this study systematically examines the relationship between population aging and housing prices, with particular attention to its dynamic effects and underlying mechanisms. The empirical findings can be summarized as follows. First, population aging exerts a positive contemporaneous effect on housing prices. Specifically, a 1% increase in the proportion of the population aged 60 and above is associated with an approximately 1.9% increase in housing prices. This result reflects the short-term demand-supporting role of aging, potentially driven by wealth effects, intergenerational transfers, and precautionary housing demand. Second, population aging significantly suppresses expected housing price growth. A 1% increase in the elderly population share leads to a 0.9% decline in anticipated housing price appreciation, indicating that aging fundamentally weakens the long-term growth momentum of the housing market. This negative expectation effect remains robust across multiple specifications. Third, heterogeneity analysis reveals that the dampening effect of population aging on housing prices is significantly stronger in regions with higher levels of economic development, more advanced industrial structures, and higher consumption capacity, consistent with a moderating-effect interpretation rather than a direct mediation pathway. Following the official ending of the one-child policy in 2015 and its subsequent relaxation, demographic pressures have not been reversed but rather intensified due to persistently low fertility rates and accelerated aging. At the same time, China’s housing market has undergone a structural shift from expansion-driven growth to adjustment and rebalancing. Rapid increases in household leverage, particularly mortgage debt, have heightened financial vulnerabilities, while government interventions—most notably the “Three Red Lines” policy introduced in 2020—have fundamentally reshaped developer financing constraints and market expectations. Within this new institutional context, the study’s findings offer important insights. The positive contemporaneous effect of population aging on housing prices helps explain why housing prices remained resilient in many cities during the early phase of demographic transition and credit expansion. However, the significant negative effect on expected price growth is consistent with the post-2020 cooling and partial restructuring of China’s real estate sector. As aging deepens and policy support shifts away from real estate-led growth, housing demand increasingly weakens, especially in economically developed regions where demographic aging and industrial upgrading jointly constrain future housing price appreciation. From a policy perspective, several implications follow. First, population aging should not be viewed as an immediate or effective tool for stabilizing housing prices. While aging may temporarily support housing demand, this effect is inherently unsustainable and is likely to reverse as demographic pressures accumulate. Second, policymakers should strengthen forward-looking demographic monitoring and early-warning systems for housing market risks. Accurate forecasting of population structure, combined with flexible housing and financial policies, is essential to mitigate the long-term downward pressure of aging on housing prices. Third, continued reforms of the pension system and household asset allocation mechanisms are necessary to reduce excessive reliance on real estate as a primary vehicle for wealth preservation. More fundamentally, addressing the long-term housing market implications of population aging requires a shift toward human-capital-centered development. Human capital plays a crucial mediating role between population aging and innovation (Cui et al., 2025). Targeted investments in education, lifelong learning, and vocational training—particularly for aging workers—can help offset the negative spillovers of aging on productivity and innovation, thereby stabilizing economic growth and housing demand. At the same time, governments, especially in regions with historically inflated housing prices, should proactively reduce dependence on land-based fiscal revenues and prepare for structural adjustments in population–housing price dynamics. Abbreviations GMM the generalized method of moments OLG Overlapping generations models M W model-Mankiw-Well model CPI Consumer Price Index Declarations Ethics approval and consent to participate This study uses publicly available city-level aggregate data and does not involve human participants or animals. Ethics approval was therefore not required. Consent for publication Not applicable. Availability of data and materials The data used in this study are drawn from publicly available sources, including the China City Statistical Yearbook, the China Urban Statistical Yearbook, and official statistical publications of the National Bureau of Statistics of China. The processed dataset is available from the corresponding author upon reasonable request. Authors contribution All authors approved the final manuscript. Competing interests The authors declare no competing interests. Funding Not applicable. Acknowledgements Not applicable. References Abel A B. 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A study on the impact of urbanization and aging on economic growth. Statistics & Decision , 2017(10): 99-103. Zou, J., Yu, T., Wang, D. A study on the regional differences of aging and housing prices—An empirical analysis based on panel cointegration models. Journal of Financial Research , 2015(11): 64-7 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9125789","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606989924,"identity":"9f1a3a56-22b1-40fa-8164-031bc4360f4f","order_by":0,"name":"Yuting Liang","email":"","orcid":"","institution":"University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Liang","suffix":""},{"id":606989926,"identity":"ae64caa9-bebb-45f4-8cbc-8c910bb47d03","order_by":1,"name":"Yike Tu","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"prefix":"","firstName":"Yike","middleName":"","lastName":"Tu","suffix":""},{"id":606989927,"identity":"e6022723-eb5c-4cd0-b063-7201ba51658f","order_by":2,"name":"Hai Zeng","email":"","orcid":"","institution":"Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Zeng","suffix":""},{"id":606989928,"identity":"742bbb26-be2d-4a63-9b28-26f522e0effd","order_by":3,"name":"Jinghang Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACxmYGBmYos/FBQoUNaVqaDR6cSSPOJqgWBjbJh22HiFDeznv4dUHNHbv+GcltFQlsBxj427sTCDiML816xrFnyTNuJLbdSOC5wyBx5uwGAlp4zIx52A4nG0iAtEg8YzCQyCVGyz+IloIEg8NEaTF+zNt22A6khSEhgTgtZsy8fYcTJM48bJZIOJDGQ9Avhv1njD/zfDtsz9+e/vDjz382cvztvQS0NDCwSQDpxAaoAA9e5SAgD4yaD0DanqDKUTAKRsEoGLkAAGycSh6e+3tzAAAAAElFTkSuQmCC","orcid":"","institution":"Western Kentucky University","correspondingAuthor":true,"prefix":"","firstName":"Jinghang","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2026-03-15 02:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9125789/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9125789/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104768231,"identity":"54817968-0bd9-47e7-b9a6-8866092438b5","added_by":"auto","created_at":"2026-03-17 04:19:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57138,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Population Aging and Housing Price Evolution in China\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9125789/v1/5d380e10e60498b5f5208410.png"},{"id":104768230,"identity":"fac9b93c-b1f5-4feb-b96f-64fb14c406f1","added_by":"auto","created_at":"2026-03-17 04:19:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37950,"visible":true,"origin":"","legend":"\u003cp\u003eContemporaneous and Lagged Effects of Population Aging on Housing Prices\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9125789/v1/36591dff6e7f3ae61b8a837c.png"},{"id":104783445,"identity":"2f01bc07-4a30-46f8-8862-27231747f81a","added_by":"auto","created_at":"2026-03-17 07:58:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1498674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9125789/v1/d6c8d0e2-cd7f-4635-9625-6158826f1776.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Effects of Population Aging on Housing Prices in China: Evidence from Prefecture-Level Cities, 2014–2023","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePopulation constitutes the bedrock of socioeconomic development, and as the primary actors in housing transactions, a certain correlation inevitably exists between population and housing prices. In 1998, the urban welfare housing allocation system was entirely supplanted by market-oriented reforms, thereby inaugurating the rapid expansion of the commercial housing market. Over the past two decades, housing prices have experienced a precipitous ascent, characterized by the real estate market\u0026rsquo;s \u0026ldquo;high prices, high demand, high supply, and high vacancy rates\u0026rdquo; (Yu et al., 2016) [1]. This phenomenon has drawn significant scrutiny from both central and local governments, prompting the enactment of policies and regulations to enforce stringent controls. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, both commercial and residential housing prices have experienced rapid increases. In 1998, the average sales prices of commercial and residential housing in China were RMB 2,063 per square meter and RMB 1,854 per square meter respectively. By 2016, these figures had surged to RMB 7,476 per square meter and RMB 7,203 per square meter, reflecting increases of 262.38% and 288.51%. Concurrently, China\u0026rsquo;s population aging has intensified, with the proportion of the population aged 65 and above standing at a mere 7.43% in 1998. However, by 2016, this proportion had risen to 10.85%, representing a 3.5 percentage point increase, thereby signifying China\u0026rsquo;s entry into an aging society according to United Nations standards. Drawing upon Maslow\u0026rsquo;s hierarchy of needs, residential demand constitutes a fundamental safety requirement. The age structure of the population exerts an influence on housing prices through both rigid demand and actual purchasing power. Given that housing prices are a critical issue with profound implications for people\u0026rsquo;s livelihoods, the impact of population aging on housing prices bears significant ramifications for the stable operation of the socioeconomic system. Consequently, elucidating the intrinsic relationship between these two factors offers a novel perspective for comprehensively understanding national population strategies and examining housing price fluctuations through the lens of population structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData Source: The data presented in this study are compiled and derived from the relevant statistical information published in the China Statistical Yearbook across the corresponding years.\u003c/p\u003e \u003cp\u003eThe relationship between population aging and housing prices has been the subject of extensive scholarly inquiry, with existing research primarily being categorized into four distinct perspectives. The first posits that population aging exerts upward pressure on housing prices. Xu Jianwei et al. (2012), drawing upon a comparative analysis of development experiences in OECD nations, observed that unlike international trends, elderly populations in China exhibit a robust demand for residential housing. This phenomenon is reflected in the concurrent rise of the old-age dependency ratio and housing prices, demonstrating a positive correlation [2]. Further substantiating this claim, Li Tongping et al. (2017) attributed 77% of housing price escalation to inertial expectations, thereby implicating population aging as a salient driver of China's elevated housing prices [3]. Conversely, the second perspective argues that population aging acts as a constraint on housing price increase. For instance, Tan Haiming et al. (2016), employing a long-term Computable General Equilibrium (CGE) model to simulate the medium-to-long-term impact of population aging on housing prices in China, concluded that demographic aging precipitates a stepwise economic deceleration, subsequently triggering a decline in real estate valuations [4]. The third perspective contends that population aging is not a deterministic factor in housing price fluctuations. Ye Yonggang et al. (2016), through the development of a housing price determination model, demonstrated that the impact of the societal elderly dependency burden on housing prices remains indeterminate[5]. Gu Hejun et al. (2017), employing the generalized method of moments (GMM) to estimate the influence of population age structure on housing consumption, found that while age structure partially explains changes in housing area, it fails to account for the sustained rise in Chinese housing prices, suggesting that future rapid increases in demand may no longer underpin housing price growth. The fourth perspective posits a dynamic relationship between aging and housing prices, characterized by spatiotemporal complexity. Influenced by specific historical policies and the \u0026lsquo;altruistic motives\u0026rsquo; of the Chinese elderly population, the policy-induced housing wealth generated by China\u0026rsquo;s housing reform policies has been gradually released through inter-generational family transfers. This in turn has facilitated the combined release of dual-generation savings into the real estate market, thereby propelling rapid urban housing price appreciation. However, as the dividends of housing reform diminish, the economic pressures of elderly care in an aging society are anticipated to reverse the housing supply-demand equilibrium, subjecting housing prices to prolonged downward pressure (Chen Guojin et al., 2013; Hu Mingzhi et al., 2017) [6\u0026ndash;7]. In summary, the relationship between population aging and housing prices remains inconclusive. Within the unique context of China\u0026rsquo;s traditional filial culture and socioeconomic landscape, the impact of population aging on housing prices warrants further in-depth investigation.\u003c/p\u003e \u003cp\u003eIn contrast to prior investigations, this study constructs a panel data model, matching housing price data with population aging data at the prefecture-level city scale. Utilizing methodologies such as the system generalized method of moments (GMM), it provides an empirical examination of the relationship between population aging and housing prices, as well as the underlying mechanisms. The marginal contributions of this research are primarily manifested in the following aspects: (1) Enhanced granularity of analysis. Due to data acquisition limitations, previous studies have predominantly utilized provinces as the unit of analysis. However, given the pronounced socio-economic sensitivity of housing prices and the substantial intra-provincial heterogeneity, employing provinces as the unit of analysis may compromise the granularity and accuracy of research findings. This study, drawing upon panel data from 250 prefecture-level cities nationwide, addresses the limitations of macro-level analyses that overlook regional internal heterogeneity, thereby offering a more nuanced and empirically grounded representation of the Chinese context. (2) Systematization of research. Previous studies have often explored the issue from isolated perspectives, such as industrial structure, purchasing power, or urbanization. This study, conversely, undertakes a systematic analysis of the multi-factorial influences of population aging on housing price fluctuations, endeavoring to situate their relationship within a unified socio-economic framework for a comprehensive investigation. (3) Deepening the depth of research. The extant literature on this subject remains relatively sparse and has yet to yield a consensus. While many studies have focused on the impact of aging on housing prices, there is a notable dearth of attention paid to the mechanisms through which population aging influences housing price dynamics.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is organized as follows: Section Two presents the research hypotheses based on theoretical insights and a literature review; Section Three outlines the econometric model, variable indicators, and data sources; Section Four reports the empirical findings; Section Five examines how the effect of population aging on housing prices varies across different socioeconomic contexts; and finally, the concluding section provides a summary of the findings and a discussion of their implications.\u003c/p\u003e"},{"header":"2 Theoretical Analysis and Research Hypothesis","content":"\u003cp\u003eReal estate, as a pivotal variable influencing national economies, has consistently remained a focal point of research both domestically and internationally. Existing studies have elucidated the determinants of housing price fluctuations from various perspectives, including land tenure systems, monetary interest rates, and speculative bubbles (Zhang Tao et al., 2008; Zhou Hui et al., 2009; Kuang Weida, 2010) [8\u0026ndash;10]. However, the explanation of housing price fluctuations cannot be confined to the interpretation of macroeconomic structures and social policies. With the escalating severity of population aging, the role of demographic factors in housing price dynamics is increasingly being recognized.\u003c/p\u003e \u003cp\u003eThe life-cycle theory elucidates the mechanism behind housing price fluctuations lies in the interplay between the population\u0026rsquo;s age structure and economic activity. To maximize lifetime utility, individuals smooth consumption and savings across different age stages, with a propensity for savings and investment during middle age, and a higher likelihood of consumption, borrowing, and asset liquidation in youth and old age (Modigliani, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) [11]. Extrapolating to the societal level, a predominance of middle-aged individuals generates excess demand in the housing market, thereby inflating housing prices. Conversely, an elevated proportion of elderly individuals reverses the housing market equilibrium of supply and demand, leading to an oversupply of housing assets and subsequent price depreciation. Bakshi et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), employing variations in the coefficient of risk aversion, arrived at similar conclusions, positing that while investors initially favor real estate investment, their pursuit of risk premium levels increases with age, resulting in a shift of wealth towards financial assets, consequently driving down housing prices and elevating stock prices [12]. Related \u0026ldquo;asset meltdown hypotheses\u0026rdquo; also posit that the elderly population\u0026rsquo;s tendency to divest assets precipitates housing price declines, potentially culminating in real estate market collapse (Poterba et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) [13].\u003c/p\u003e \u003cp\u003eMankiw et al. (1989), employing a dynamic asset demand-supply model (the Mankiw-Well model) to estimate housing demand parameters based on age structure, observed a \u0026lsquo;hump-shaped\u0026rsquo; pattern in individual life-cycle housing demand, characterized by a low demand level prior to age 20, a significant surge to peak demand between ages 20 and 30, and a subsequent decline at a rate of 1% per annum after age 40 [14]. Subsequent scholarly investigations have subjected the M-W model to extensive scrutiny. Ermisch (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) demonstrated a significant relationship between the UK population structure and residential demand growth, indicating that population aging would attenuate the rate of increase in residential housing demand [15]. Ohtake et al. (1996), examining interplay between Japan\u0026rsquo;s demographic structure and the temporal dynamics of the real estate market, revealing that short-term population fluctuations substantially affect housing price fluctuations [16]. Furthermore, studies utilizing overlapping generations models (OLG) have revealed that the aging of the post-World War II \u0026ldquo;baby boom\" generation contributed to asset price declines and a reduction in capital returns in the United States (Abel, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Krueger et al., 2006) [17\u0026ndash;18]. Analogous findings have been reported in studies conducted in regions such as Ireland (Martin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) [19].\u003c/p\u003e \u003cp\u003eAs evidenced by the aforementioned literature, a complex relationship exists between population aging and housing prices. Within the contemporary Chinese context, four decades of reform and opening-up have resulted in the accumulation of wealth among a segment of the population, which is now predominantly middle-aged to elderly. Given the appreciation of housing prices, this demographic group is inclined to utilize real estate as a means of asset preservation and appreciation, thereby exerting upward influence on housing prices. However, with the progressive intensification of societal aging, the economic burden on the working-age population increases, potentially leading to a decline in optimistic expectations regarding future housing prices. This in turn may manifest as a decrease in anticipated housing prices within the real estate market. Consequently, we propose the following hypothesis H1:\u003c/p\u003e \u003cp\u003eH1: Population aging has time-varying effects on housing prices: it may raise current housing prices in the short run but depress subsequent housing price growth over time.\u003c/p\u003e \u003cp\u003eThe existing literature does not offer a unanimous view regarding the magnitude and mechanisms through which population aging influences housing prices. Existing research has primarily identified the following mechanisms: Firstly, the inherent characteristics of real estate, including land scarcity, high investment returns, and universal housing demand, endow housing consumption with dual attributes of both consumption and investment (Carliner, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) [20]. Consequently, real estate prices are fundamentally anchored in macroeconomic development levels (Shen Yue et al., 2002) [21], with China\u0026rsquo;s rapid economic growth exerting a decisive influence on housing price appreciation (Lin Yifu et al., 2003) [22]. The sustained economic growth in China has stimulated urban housing demand, thereby propelling housing price escalation (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) [23]. Conversely, population aging exerts a significant negative impact on economic growth through its effects on labor supply (Zhu Yuepu et al., 2017) [24], human capital (Zhang Xiuwu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) [25], and material capital investment (Song Qi et al., 2017) [26]. This suggests that the level of regional economic development may condition the magnitude of the effect of population aging on housing prices. Consequently, we propose the following hypothesis H2a:\u003c/p\u003e \u003cp\u003eH2a: The effect of population aging on housing prices is moderated by the level of regional economic development.\u003c/p\u003e \u003cp\u003eSecondly, the advancement and rationalization of industrial structure exert a positive influence on urban housing prices (Fan et al., 2011; Fan et al., 2018) [27\u0026ndash;28]. Malpezzi (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), using the United States as a case study, found that a 1% increase in industrial agglomeration correlates with a 28% rise in real estate prices [29]. However, research by Roger and Wasmer (2012) suggests that population aging diminishes labor force adaptability [30], thereby constraining industrial agglomeration. The aging of the labor force also indirectly impedes the upgrading of industrial structures (Yang Xue et al., 2011) [31]. The exacerbation of societal aging hampers the optimization and upgrading of industrial structures through labor productivity and elderly dependency burden effects (Wang Wei et al., 2015) [32]. Drawing upon the aforementioned research, it is evident that population aging exerts a negative impact on industrial structure, while industrial structure positively influences housing prices. Consequently, we propose the following hypothesis: H2b:\u003c/p\u003e \u003cp\u003eH2b: The effect of population aging on housing prices is moderated by local industrial structure.\u003c/p\u003e \u003cp\u003eFinally, it is the economic attributes of the population, rather than its natural attributes, that influence housing prices (Wang Sheng et al., 2017) [33]. The relationship between housing prices and population age structure is also contingent upon disparities in actual consumption capacity. From the perspective of inter-generational savings transfer, parental financial assistance constitutes a substantial proportion of housing purchases arising from declining household sizes (Ren Wei et al., 2016) [34]. Parental subsidies for children\u0026rsquo;s housing acquisition result in a reduction of elderly individuals\u0026rsquo; personal assets and a decline in their actual consumption capacity. This phenomenon is particularly pronounced in eastern regions, where the contradiction between youth low-consumption capacity and high housing prices is acute. Furthermore, from the perspective of societal demographic transition, when societal aging progresses into a demographic debt phase, society may transition into a \u0026lsquo;low-desire\u0026rsquo; state characterized by decreased fertility, delayed marriage, and reduced home-ownership, leading to diminished consumption levels and downward pressure on housing prices (Li Tongping et al., 2017) [3]. Population aging can lead to a decline in regional consumption levels, which in turn directly affects regional housing prices. Consequently, we propose the following hypothesis: H2c:\u003c/p\u003e \u003cp\u003eH2c: The effect of population aging on housing prices is moderated by regional consumption capacity.\u003c/p\u003e"},{"header":"3 Research Design","content":"\u003cp\u003e3.1 Econometric Model\u003c/p\u003e\n\u003cp\u003eConsidering the issue of endogeneity, this study employs the system generalized method of moments (GMM) to test the hypotheses outlined above. The specific model is as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e3.2 Variable Selection\u003c/p\u003e\n\u003cp\u003eDependent Variables: Housing prices (\u003cem\u003ehp\u003c/em\u003e), specifically commercial housing prices (\u003cem\u003ehp1\u003c/em\u003e) and residential housing prices (\u003cem\u003ehp2\u003c/em\u003e). Both commercial and residential housing prices are calculated by dividing the total sales revenue by the total sales area. Commercial housing prices are used for the baseline regression, while residential housing prices are employed for robustness checks. Prior to model regression, nominal housing price data are deflated using the Consumer Price Index (CPI) to obtain real housing price values.\u003c/p\u003e\n\u003cp\u003eCore Independent Variable: Population aging (\u003cem\u003eage\u003c/em\u003e). Following the United Nations\u0026rsquo; criteria for an aging society and commonly used metrics in academic literature, key indicators for measuring regional population aging include the proportion of the population aged 65 and above, old-age dependency ratio, and median age (Lin Lin, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) [35]. This study utilizes the proportion of the population aged 60 and above (\u003cem\u003eage60\u003c/em\u003e) and the proportion of the population aged 65 and above (\u003cem\u003eage65\u003c/em\u003e) to represent the level of regional population aging.\u003c/p\u003e\n\u003cp\u003eIn addition to the core independent variable, control variables are selected based on relevant literature and the identified determinants of housing prices. These variables are categorized into macroeconomic environment, population socio-economic attributes, and regional public resource allocation.\u003c/p\u003e\n\u003cp\u003e(1) From the angle of macroeconomic environment, while population aging may reduce overall housing demand, economic growth itself stimulates housing acquisition demand (Farkas, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) [36]. Therefore, this study employs variables such as per capita GDP (ln\u003cem\u003epgdp\u003c/em\u003e), the proportion of secondary industry (\u003cem\u003esepconsumd\u003c/em\u003e), the proportion of tertiary industry (\u003cem\u003ethird\u003c/em\u003e), total bank loans (\u003cem\u003ebankloan\u003c/em\u003e), and the proportion of real estate investment in total investment (\u003cem\u003ehou_inv\u003c/em\u003e) to characterize the regional macroeconomic environment. Generally, higher levels of regional economic development and monetary easing stimulate social housing demand, leading to housing price appreciation (Martin, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) [19].\u003c/p\u003e\n\u003cp\u003e(2) From the perspective of population socio-economic attributes: This study employs indicators such as population density (ln\u003cem\u003epopren\u003c/em\u003e), average wage (ln\u003cem\u003ewage\u003c/em\u003e), and per capita consumption expenditure (ln\u003cem\u003epconsum\u003c/em\u003e) to characterize the socio-economic attributes of the population. Due to land scarcity, the growth rate of housing stock cannot keep pace with population growth. The limited housing capacity per unit area results in housing price escalation with increasing population density. Furthermore, the environmental pressures associated with population growth contribute to higher real estate development complexity and costs, thereby driving up housing prices (Wu Lichao et al., 2018) [37]. From an economic investment and expectation perspective, the high asset preservation and inflation-hedging properties of real estate render it a valuable tool for household wealth preservation and appreciation (Gao Bo et al., 2013) [38]. Consequently, indicators of individual economic well-being, such as wages and consumption expenditure, warrant consideration.\u003c/p\u003e\n\u003cp\u003e(3) From the perspective of regional public resource allocation, within the framework of housing economics theory, housing demand exhibits hierarchical characteristics. Beyond basic infrastructure needs such as shelter, demand arises for location environment amenities, including favorable employment opportunities and high-quality public services (Rosen, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) [39]. Housing prices reflect a composite of structural and location environment\u0026rsquo;s attributes, with a higher provision of quality public goods increasing consumers\u0026rsquo; willingness to pay, thereby driving up housing prices. To this end, this study employs the number of university students per 10,000 residents (ln\u003cem\u003estu\u003c/em\u003e), the volume of library books per 10,000 residents (ln\u003cem\u003elib\u003c/em\u003e), and the number of hospital beds per 10,000 residents (ln\u003cem\u003emed\u003c/em\u003e) as indicators of regional education, cultural, and medical resource allocation. These variables serve to quantify the impact of regional public resource allocation differentials on housing prices.\u003c/p\u003e\n\u003cp\u003e3.3 Data Sources\u003c/p\u003e\n\u003cp\u003eHousing price data are mainly drawn from the China City Statistical Yearbook, which provides city-level information on housing sales revenue and sales area. Population aging indicators are constructed from prefecture-level population age-structure statistics published by the National Bureau of Statistics of China. The remaining control variables are obtained from the China City Statistical Yearbook and the China Urban Statistical Yearbook for the corresponding years.\u003c/p\u003e\n\u003cp\u003eAfter matching the housing-price data with the population-aging indicators and excluding cities with substantial missing observations, we obtain an unbalanced panel of approximately 250 prefecture-level cities covering the period 2014\u0026ndash;2023. To reduce the influence of extreme values, key continuous variables are winsorized at the 1st and 99th percentiles.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reports the descriptive statistics of the main variables. According to the international criteria for an aging society\u0026mdash;defined as a society in which the proportion of the population aged 60 and above exceeds 10%, or the proportion aged 65 and above exceeds 7%\u0026mdash;China had fully entered an aging society during the sample period. Specifically, during 2014\u0026ndash;2023, the average proportions of the population aged 60 and above and 65 and above reached 21.1% and 15.4%, respectively, indicating a deepening stage of population aging. In cities experiencing the most severe population aging, the proportion of elderly residents exceeded 24% for the population aged 60 and above and 17% for those aged 65 and above, underscoring the growing demographic pressure associated with rapid aging. Moreover, housing prices exhibit substantial regional heterogeneity across cities. The logarithm of real housing prices ranges from approximately 5.99 to 10.90, reflecting pronounced disparities in regional housing market conditions.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample Size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eDependent Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of commercial housing prices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of residential housing prices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eage60\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of the population aged 60 and above (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eage65\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of the population aged 65 and above (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epgdp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003esecond\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of secondary industry (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ethird\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of tertiary industry (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ebankloan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of total bank loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehou_inv\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of real estate investment in total investment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epopren\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of population density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ewage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of average wage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epconsum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of per capita consumption expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003estu\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of the number of university students per 10,000 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003elib\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of the number of books per 10,000 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003emed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural logarithm of the number of hospital beds per 10,000 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4 Results Analysis","content":"\u003cp\u003e4.1 Baseline Regression\u003c/p\u003e\n\u003cp\u003eBased on Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), we estimate the model using the System GMM approach, and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the baseline regression results. Among the six sets of estimates reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the first four models employ the proportion of the population aged 60 and above as well as that of individuals aged 65 and above\u0026mdash;both without and with additional control variables\u0026mdash;for the estimation of commercial housing prices. For these four models, the P-values for the AR(2) tests are not statistically significant, and the Sargan test P-values are also insignificant, which indicates that the System GMM estimates are reliable. Moreover, the Wald Chi2 test results further confirm the validity of the estimations for these four models. Notably, the one-period lagged housing price variable is non-significant in these four models, suggesting a limited degree of path dependency in housing prices.\u003c/p\u003e\n\u003cp\u003eThe relationship between the proportion of the population aged 60 and above and commercial housing prices is examined in column (1). The results indicate that population aging has a significantly positive effect at the 1% level. This indicates that population aging exerts a positive influence on commercial housing prices; specifically, a 1% increase in the proportion of the population aged 60 and above is associated with a 1.9% increase in housing prices. Upon the inclusion of other control variables in column (2), the significance of the population aging variable remains at the 1% level, and the coefficient exhibits no substantial change, suggesting a robust effect of population aging on commercial housing prices. Columns (3) and (4) present estimates using the proportion of the population aged 65 and above as the core independent variable. Column (3) demonstrates a statistically significant positive relationship between the proportion of the population aged 65 and above and commercial housing prices. Following the inclusion of control variables in column (4), the coefficient remains statistically significant and positive, with a value of 0.018, indicating that a 1% increase in the proportion of the population aged 65 and above is associated with a 1.8% increase in housing prices.\u003c/p\u003e\n\u003cp\u003eGiven the findings of Li Tongping et al. (2017), which suggest that anticipated population aging exerts a suppressive effect on housing price appreciation [3], we examined the impact of one-period lagged population aging on current housing prices. This analysis aims to understand the influence of current population aging on future housing prices\u0026mdash;in other words, its effect on housing prices. Columns (5) and (6) present the corresponding estimation results. Both lagged aging indicators exhibit a significant impact on commercial housing prices, albeit with a reversal in the direction of influence. Specifically, population aging demonstrates a negative relationship with housing prices, indicating that a 1% increase in the proportion of the population aged 60 and above is associated with a 0.9% decrease in anticipated housing prices. Similarly, a 1% increase in the proportion of the population aged 65 and above is associated with a 1.5% decrease in anticipated housing prices. This reversal in coefficient sign suggests that the impact of population aging on housing prices in China is not unidirectional. The phenomenon of housing prices exhibiting an \u0026lsquo;initial increase followed by a subsequent decrease\u0026rsquo; in relation to population aging has also been corroborated by international scholars. Eichholtz and Lindenthal (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), in their study of the United Kingdom, found that the suppressive effect of population aging on housing demand is conditional, with a notable decline in demand occurring primarily towards the terminal phase of elderly individuals\u0026rsquo; life cycles [40]. However, the impact of population aging on housing price fluctuations in China is characterized by greater complexity and is deeply intertwined with the nation\u0026rsquo;s unique historical and socio-economic context. The demographic growth patterns associated with an aging society exhibit differentiation. During the incipient stages of aging, the overall population continues to experience growth, albeit at a decelerating rate. It is only in the advanced stages of aging that a stationary population, or even population decline, becomes evident (Li Tongping et al., 2017) [3]. From the perspective of familial structural evolution, the principle of \u0026lsquo;one household requiring one residence\u0026rsquo; implies that population aging and the nucleation of families have generated a substantial number of elderly individuals living alone, thereby augmenting housing demand. However, the increased dependency of aging populations on their offspring may lead to a higher probability of cohabitation. This dynamic, as reflected in the real estate market, suggests that housing demand declines only when elderly individuals return to reside with their children, resulting in a reduction in the number of households. Consequently, scholars have posited an \u0026lsquo;inverted U-shaped\u0026rsquo; relationship between population aging and housing demand, wherein the stimulative effect of aging on housing demand is positive during the incipient stages, but reverses direction upon reaching a critical threshold (Ding Yang et al., 2018) [41]. Furthermore, considering the broader socio-political policy context, the demographic age structure transition in China coincided with the implementation of housing policy reforms. The historical wealth generated by the large-scale privatization of public housing during the housing reform period has been subject to long-term inter-generational transfers. The concurrent implementation of the one-child policy, which resulted in a reduction in the number of offspring, further amplified the inter-generational transfer effect of welfare housing, leading to a pronounced prevalence of elderly individuals providing financial assistance for housing purchases (Hu Mingzhi, 2017) [7]. It is posited that the suppressive effect of population aging on housing prices will become prominent only upon the complete dissipation of the housing reform dividends. Consequently, hypothesis H1 is substantiated, indicating that the current demographic aging in China exerts an upward influence on housing prices. However, with the progressive intensification of aging and the ongoing adjustments associated with China\u0026rsquo;s economic entering a new normal, the inhibitory influence of population aging on housing prices is expected to become increasingly pronounced.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline regression results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eage60\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eage65\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n 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align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL.\u003cem\u003eage65\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epgdp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n 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align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.352)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.374)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ethird\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ebankloan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehou_inv\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epopden\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ewage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epconsum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003estu\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003elib\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003emed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant Terms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.153\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.656\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.171\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.635\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.642\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.645\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.568)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.570)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.573)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2) P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSargen P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWald Chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9226.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7335.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9146.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7354.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7063.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6944.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: Standard errors are presented in parentheses. Values within square brackets represent p-values for the F-test, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, this convention applies throughout the document.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding other control variables: (1) In terms of the macroeconomic environment, per capita GDP exerts a positive influence on housing prices, indicating that regional economic development drives housing price appreciation. Specifically, for a 1% increase in per capita GDP, housing prices rise by 0.8% to 0.9%. The influence of various industries on housing prices differs. The relationship between the proportion of secondary industry and housing prices does not achieve statistical significance. Conversely, the proportion of tertiary industry demonstrates a significant negative relationship with housing prices at the 1% level, suggesting that a 1% increase in the proportion of tertiary industry is associated with a 0.5% to 0.6% decrease in housing prices. The correlation between the proportion of real estate investment in total investment and housing prices is statistically significant at the 5% level, with a coefficient of 0.185, indicating that a 1% increase in the proportion of real estate investment leads to a substantial 18.5% increase in housing prices. This phenomenon may be attributed to the fact that rapid housing price appreciation implies higher returns on real estate investment. The allure of profitability and expectations of further price escalation incentivize households to increase real estate investments, thereby contributing to the elevation of housing prices. Upon the inclusion of the lagged aging variable, the coefficient increases to 20.4%. (2) Regarding population socio-economic attributes, population density, average wage, and per capita consumption expenditure do not achieve statistical significance. However, the coefficients for all three variables exhibit a positive relationship with housing prices. (3) Concerning regional public resource allocation, the relationship between the number of hospital beds per 10,000 residents and housing prices is statistically significant at the 1% level. A 1% increase in the number of hospital beds is associated with a 0.88% to 0.92% increase in housing prices. This suggests that public resources are capitalized and integrated into housing price valuation, generating a premium effect in the real estate market. The magnitude of this premium is contingent upon the uneven distribution of public goods across cities, with housing prices in prime locations typically being higher, hence the positive coefficient.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the estimated marginal effects of population aging on commercial housing prices, distinguishing between contemporaneous and one-period lagged effects. The points represent coefficient estimates derived from the System GMM baseline regressions, and the vertical bars denote 95% confidence intervals. The results indicate that population aging exerts a positive contemporaneous effect on housing prices, while its lagged effect is significantly negative, highlighting pronounced time heterogeneity in the impact of aging on housing markets.\u003c/p\u003e\n\u003cp\u003e4.2 Robustness Check Using Residential Housing Prices\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of the baseline results, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reports a set of alternative estimations in which residential housing prices (lnhp2) are employed as a substitute for commercial housing prices (lnhp1). Compared with commercial housing prices, residential housing prices are more closely related to actual housing demand and therefore provide a more demand-oriented perspective for validating the empirical findings. The results indicate that the core conclusions remain largely unchanged. Consistent with the baseline estimations, current population aging continues to exert a positive effect on residential housing prices, while one-period lagged population aging exhibits a significant negative effect, confirming the coexistence of contemporaneous stimulative effects and lagged suppressive effects of population aging on housing prices. This finding suggests that the impact of aging on housing prices is robust across different housing market segments.\u003c/p\u003e\n\u003cp\u003eSpecifically, column (1) examines the effect of the proportion of the population aged 60 and above on residential housing prices. The coefficient remains positive and statistically significant at the 10% level, indicating that a 1% increase in the elderly population share is associated with a 0.7% increase in residential housing prices. Although the magnitude of the coefficient is smaller than that in the baseline regression, the direction and significance remain consistent, implying that population aging continues to support housing prices, albeit with a weaker effect in demand-oriented residential markets. Column (2) reports the estimation results using the proportion of the population aged 65 and above as the key explanatory variable. The coefficient is positive and significant at the 10% level, suggesting that a 1% increase in the population aged 65 and above raises residential housing prices by approximately 1.1%. Columns (3) and (4) further investigate the impact of population aging on housing prices by introducing one-period lagged aging variables. The results show that lagged aging exerts a statistically significant negative effect on residential housing prices. A 1% increase in the lagged proportion of the population aged 60 and above and 65 and above leads to a 0.2% and 0.4% decrease in current residential housing prices, respectively. These findings reinforce the baseline conclusion that while population aging supports housing prices in the short term, it generates downward pressure on future housing prices by weakening long-term demand expectations.\u003c/p\u003e\n\u003cp\u003eIn addition, the one-period lagged residential housing price exhibits a significant negative coefficient, suggesting that residential housing prices display weaker price persistence and are more responsive to changes in market fundamentals. Compared with the baseline regression, the statistical significance of several control variables is enhanced. In particular, the proportion of secondary industry and average wage demonstrate significantly positive effects at the 1% level, while library book holdings per 10,000 residents exhibit a negative effect at the 10% level. Moreover, the coefficient of the proportion of real estate investment in total investment increases substantially, ranging from 13.9% to 17.5%, indicating that residential housing prices are more sensitive to socio-economic conditions and investment dynamics. The comparison between Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that replacing commercial housing prices with residential housing prices does not alter the main empirical conclusions. Instead, it further confirms the robustness of the estimated effects and highlights that the influence of population aging is more pronounced in investment-driven housing markets, while residential housing prices respond more strongly to underlying economic and social fundamentals.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRobustness checks using residential housing prices\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL.ln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eage60\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eage65\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL.\u003cem\u003eage60\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL.\u003cem\u003eage65\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epgdp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003esecond\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.171\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.228\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.225\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.258\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ethird\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebankloan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehou_inv\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.360\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.343\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.357\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epopden\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003ewage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003epconsum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003estu\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003elib\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln\u003cem\u003emed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant Terms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.438\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.392\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.437\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.489\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.545)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2) P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSargen P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"5 Heterogeneity Analysis: Moderating Effects of Socioeconomic Context","content":"\u003cp\u003eSignificant regional development imbalances exist within China, manifested not only in economic development environments but also in the socio-economic attributes of its population. Consequently, the following question arises: Does the impact of population aging on housing prices vary across different socio-economic contexts? Population aging may interact with regional economic development, thereby influencing housing price fluctuations. To address this question, this section examines an extension of the primary research proposition by incorporating interaction items into the original regression equation. The results of this analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the regression results obtained by including interaction items between population aging indicators and per capita GDP, industrial structure, and consumption levels as explanatory variables. The findings indicate that, regardless of whether population aging is measured using the 60-year-old or 65-year-old criteria, the coefficients of the interaction items are consistently negative and statistically significant at the 5% level or higher. Consequently, the aforementioned regression results indicate a significant negative correlation between population aging and socio-economic development. Specifically, under the condition of controlling for other variables, the higher the level of socio-economic development in a region, the stronger the inhibitory effect of population aging on housing price appreciation. In column (1), while the coefficients for both age60 and ln\u003cem\u003epgdp\u003c/em\u003e are positive, the estimated coefficient for age60*ln\u003cem\u003epgdp\u003c/em\u003e is negative and statistically significant at the 5% level. This result suggests that although both population aging and economic development exert a positive influence on housing prices, their interaction items demonstrates a negative influence. This is attributed to the negative impact of population aging on economic development levels, thereby suggesting that the dampening effect of aging on housing prices is stronger in more economically developed regions, consistent with hypothesis H2a. Regarding the interaction items between population aging and industrial structure, the regression results in columns (2) and (3) consistently demonstrate a negative estimated coefficient, which is statistically significant at the 1% level for both. These results validate the potential interaction between population aging and industrial structure, as well as its inhibitory effect on housing price appreciation, which aligns with the arguments presented in hypothesis H2b. Column (4) examines the relationship between housing prices and the interaction items of population aging and consumption levels. The results indicate that the coefficient is statistically significant at the 1% level and negative, suggesting that consumption levels moderate the relationship between population aging and housing prices. Specifically, in regions with higher levels of population aging, the positive correlation between aging and housing price appreciation, as mediated by the consumption interaction items, is suppressed. This provides empirical support for hypothesis H2c. In this context, the estimation results of this regression not only align with the findings of existing literature but also contribute to a more nuanced understanding of the complex interplay between population aging, socio-economic indicators, and housing price fluctuations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModerating effects: commercial housing prices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.ln\u003cem\u003ehp1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*\u003c/em\u003eln\u003cem\u003epgdp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*second\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.195\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*third\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*\u003c/em\u003eln\u003cem\u003epconsum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.025\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.286\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*\u003c/em\u003eln\u003cem\u003epgdp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*second\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*third\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*\u003c/em\u003eln\u003cem\u003epconsum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant Terms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.970\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.163\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.281\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.096\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.498\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.505\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.844\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.568)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.692)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2) P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSargen P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald Chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7502.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7559.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7443.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7965.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7842.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7642.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7532.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7491.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, to further ascertain the reliability of the regression estimation results, this section employs residential housing prices as an alternative measure of the dependent variable, housing prices, to conduct additional robustness analyses. The resulting findings are presented below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModerating effects: residential housing prices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.ln\u003cem\u003ehp2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.015\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.016\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.016\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.016\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*\u003c/em\u003eln\u003cem\u003epgdp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*second\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.190\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*third\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage60*\u003c/em\u003eln\u003cem\u003epconsum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.358\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*\u003c/em\u003eln\u003cem\u003epgdp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*second\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*third\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage65*\u003c/em\u003eln\u003cem\u003epconsum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.036\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant Terms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.462\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.683\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.798\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.210\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.762\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.992\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.903\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.687\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.542)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.565)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.765)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2) P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSargen P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald Chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9272.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9925.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10011.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10237.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9554.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10146.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10127.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10145.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents regression results consistent with those in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Columns (1) to (4) display regression results where the interaction items including the percentage of the population aged 60 and above and per capita GDP, industrial structure, and consumption level are used as primary explanatory variables, with other control variables included. Columns (5) to (8) replace the population aging variable with the percentage of the population aged 65 and above for interaction item analysis. Even after employing alternative indicators, the impact of population aging on housing prices remains significant at the 1% level, with coefficients showing minimal variation. The interaction items consistently exhibit significant negative coefficients at the 1% level, reinforcing the robustness of the regression results.\u003c/p\u003e"},{"header":"6 Summary and Discussion","content":"\u003cp\u003ePopulation aging has become one of the most profound structural challenges confronting China\u0026rsquo;s long-term socio-economic sustainability. Housing prices, as a central indicator of macroeconomic performance and household wealth accumulation, constitute a critical linkage between demographic dynamics and economic activity (Zou Jin et al., 2015). Using panel data from 250 prefecture-level cities, this study systematically examines the relationship between population aging and housing prices, with particular attention to its dynamic effects and underlying mechanisms.\u003c/p\u003e \u003cp\u003eThe empirical findings can be summarized as follows. First, population aging exerts a positive contemporaneous effect on housing prices. Specifically, a 1% increase in the proportion of the population aged 60 and above is associated with an approximately 1.9% increase in housing prices. This result reflects the short-term demand-supporting role of aging, potentially driven by wealth effects, intergenerational transfers, and precautionary housing demand. Second, population aging significantly suppresses expected housing price growth. A 1% increase in the elderly population share leads to a 0.9% decline in anticipated housing price appreciation, indicating that aging fundamentally weakens the long-term growth momentum of the housing market. This negative expectation effect remains robust across multiple specifications. Third, heterogeneity analysis reveals that the dampening effect of population aging on housing prices is significantly stronger in regions with higher levels of economic development, more advanced industrial structures, and higher consumption capacity, consistent with a moderating-effect interpretation rather than a direct mediation pathway.\u003c/p\u003e \u003cp\u003eFollowing the official ending of the one-child policy in 2015 and its subsequent relaxation, demographic pressures have not been reversed but rather intensified due to persistently low fertility rates and accelerated aging. At the same time, China\u0026rsquo;s housing market has undergone a structural shift from expansion-driven growth to adjustment and rebalancing. Rapid increases in household leverage, particularly mortgage debt, have heightened financial vulnerabilities, while government interventions\u0026mdash;most notably the \u0026ldquo;Three Red Lines\u0026rdquo; policy introduced in 2020\u0026mdash;have fundamentally reshaped developer financing constraints and market expectations.\u003c/p\u003e \u003cp\u003eWithin this new institutional context, the study\u0026rsquo;s findings offer important insights. The positive contemporaneous effect of population aging on housing prices helps explain why housing prices remained resilient in many cities during the early phase of demographic transition and credit expansion. However, the significant negative effect on expected price growth is consistent with the post-2020 cooling and partial restructuring of China\u0026rsquo;s real estate sector. As aging deepens and policy support shifts away from real estate-led growth, housing demand increasingly weakens, especially in economically developed regions where demographic aging and industrial upgrading jointly constrain future housing price appreciation.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, several implications follow. First, population aging should not be viewed as an immediate or effective tool for stabilizing housing prices. While aging may temporarily support housing demand, this effect is inherently unsustainable and is likely to reverse as demographic pressures accumulate. Second, policymakers should strengthen forward-looking demographic monitoring and early-warning systems for housing market risks. Accurate forecasting of population structure, combined with flexible housing and financial policies, is essential to mitigate the long-term downward pressure of aging on housing prices. Third, continued reforms of the pension system and household asset allocation mechanisms are necessary to reduce excessive reliance on real estate as a primary vehicle for wealth preservation.\u003c/p\u003e \u003cp\u003eMore fundamentally, addressing the long-term housing market implications of population aging requires a shift toward human-capital-centered development. Human capital plays a crucial mediating role between population aging and innovation (Cui et al., 2025). Targeted investments in education, lifelong learning, and vocational training\u0026mdash;particularly for aging workers\u0026mdash;can help offset the negative spillovers of aging on productivity and innovation, thereby stabilizing economic growth and housing demand. At the same time, governments, especially in regions with historically inflated housing prices, should proactively reduce dependence on land-based fiscal revenues and prepare for structural adjustments in population\u0026ndash;housing price dynamics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe generalized method of moments\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOLG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverlapping generations models\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eW model-Mankiw-Well model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsumer Price Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study uses publicly available city-level aggregate data and does not involve human participants or animals. Ethics approval was therefore not required.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data used in this study are drawn from publicly available sources, including the China City Statistical Yearbook, the China Urban Statistical Yearbook, and official statistical publications of the National Bureau of Statistics of China. The processed dataset is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAuthors contribution\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbel A B. Will Bequests Attenuate the Predicted Meltdown in Stock Prices When Baby Boomers Retire? \u003cem\u003eReview of Economics \u0026amp; Statistics\u003c/em\u003e, 2001, 83(4):589-595.\u003c/li\u003e\n \u003cli\u003eBakshi G S, Chen Z. Baby Boom, Population Aging, and Capital Markets. \u003cem\u003eJournal of Business\u003c/em\u003e, 1994, 67(2):165-202.\u003c/li\u003e\n \u003cli\u003eCarliner G. 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A study on the regional differences of aging and housing prices\u0026mdash;An empirical analysis based on panel cointegration models.\u0026nbsp;\u003cem\u003eJournal of Financial Research\u003c/em\u003e, 2015(11): 64-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Population Aging, Housing Prices, Dynamic Effects, Urban Heterogeneity, System GMM","lastPublishedDoi":"10.21203/rs.3.rs-9125789/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9125789/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePopulation aging is emerging as an increasingly important force shaping urban housing markets, yet its impact on housing prices remains theoretically contested and empirically inconclusive. Using an unbalanced panel of approximately 250 prefecture-level cities in China from 2014 to 2023, this study investigates the dynamic effects of population aging on housing prices. To address price persistence, unobserved city heterogeneity, and potential endogeneity between demographic structure and housing markets, we employ dynamic panel models estimated by the system generalized method of moments (System GMM).\u003c/p\u003e \u003cp\u003eThe results reveal a clear time-varying relationship between population aging and housing prices. Contemporaneous aging is positively associated with both commercial and residential housing prices, suggesting that in the short run, aging may still sustain housing demand through channels such as accumulated household wealth, intergenerational support, and family-based housing arrangements. In contrast, lagged aging exerts a significantly negative effect on housing prices, indicating that deeper demographic aging may gradually weaken medium-term housing demand and slow subsequent housing price growth.\u003c/p\u003e \u003cp\u003eFurther analysis shows that these effects vary across urban socioeconomic contexts. The negative lagged effect of aging is more pronounced in cities with higher levels of economic development, more advanced industrial structures, and stronger consumption capacity. This suggests that the housing-market consequences of population aging are shaped not only by timing, but also by local structural conditions.\u003c/p\u003e \u003cp\u003eThis study contributes to the literature by providing city-level evidence on the dynamic and context-dependent effects of population aging on housing prices in China. The findings also offer policy implications for cities confronting both demographic transition and housing market adjustment.\u003c/p\u003e","manuscriptTitle":"Dynamic Effects of Population Aging on Housing Prices in China: Evidence from Prefecture-Level Cities, 2014–2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 04:19:22","doi":"10.21203/rs.3.rs-9125789/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7a163f5-477a-42f7-99b7-4a9b1426e859","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"296538082664721294592194910186165510302","date":"2026-05-14T14:07:57+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"26441932564606171125090032991267712959","date":"2026-05-14T12:43:16+00:00","index":18,"fulltext":""},{"type":"reviewerAgreed","content":"27000970902702635154573800163045145471","date":"2026-05-13T05:16:19+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"36886592826801187565378565602469932015","date":"2026-05-12T12:28:32+00:00","index":16,"fulltext":""},{"type":"reviewersInvited","content":"6","date":"2026-05-12T12:21:32+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T12:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 04:19:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9125789","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9125789","identity":"rs-9125789","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}