# RECORD 54da27518406c538ad01d44349b80f8a974bfe46 # TITLE: Economic effects of air quality on housing prices: evidence from Beijing, China # YEAR: 2024.0 VENUE: Journal of Housing and the Built Environment DOI: 10.1007/s10901-023-10108-z # AUTHORS: Yuanyuan Cai; Martijn J. Smit; M. Helbich # PAGES: 24 CANDIDATE TABLE PAGES (1-based): [14, 15, 16, 18, 19, 20, 21] # ABSTRACT: Air pollution is a major environmental urban issue, particularly in fast-growing cities in developing countries. Reducing air pollution is thus a challenge while evaluating the economic value of air quality is crucial for environmental policies made. However, few studies accurately estimate this value as they neglect the possible endogeneity issues, as well as the dynamic and heterogeneous effects of air pollution. Under the hedonic framework, we therefore assess the economic effect of fine particulate matter (PM2.5) on housing prices in Beijing, China. We construct a panel based on resale apartment transactions matched with average quarterly PM2.5 data between 2013 and 2019. To reduce the risk of an estimation bias, we apply an instrumental variable (IV) approach. Our results show that PM2.5 is negatively associated with housing prices. Households were willing to pay an extra 0.0852% per housing unit price for an average quarterly reduction in PM2.5 of 1 µg/m3. Furthermore, we argue that high-income dwellers tend to pay more for clean air. The negative effects of PM2.5 across regions are significant and different. Compared with that in the basic year 2013, the negative effect increases in the first 3 years and then decreases in the last 3 years. Our findings enhance our comprehension of the economic impact of air quality and make a valuable contribution to the nuanced understanding of willingness to pay for air quality, which is beneficial in assessing and optimizing environmental regulations. ===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) ===== Vol.:(0123456789) Journal of Housing and the Built Environment (2024) 39:885–908 https://doi.org/10.1007/s10901-023-10108-z 1 3 ARTICLE Economic effects of air quality on housing prices: evidence from Beijing, China Yuanyuan Cai1,2   · Martijn J. Smit2   · Marco Helbich2  Received: 3 October 2022 / Accepted: 22 December 2023 / Published online: 29 January 2024 © The Author(s) 2024 Abstract Air pollution is a major environmental urban issue, particularly in fast-growing cities in developing countries. Reducing air pollution is thus a challenge while evaluating the eco- nomic value of air quality is crucial for environmental policies made. However, few studies accurately estimate this value as they neglect the possible endogeneity issues, as well as the dynamic and heterogeneous effects of air pollution. Under the hedonic framework, we therefore assess the economic effect of fine particulate matter ­(PM2.5) on housing prices in Beijing, China. We construct a panel based on resale apartment transactions matched with average quarterly ­PM2.5 data between 2013 and 2019. To reduce the risk of an estimation bias, we apply an instrumental variable (IV) approach. Our results show that ­PM2.5 is nega- tively associated with housing prices. Households were willing to pay an extra 0.0852% per housing unit price for an average quarterly reduction in ­PM2.5 of 1 µg/m3. Furthermore, we argue that high-income dwellers tend to pay more for clean air. The negative effects of ­PM2.5 across regions are significant and different. Compared with that in the basic year 2013, the negative effect increases in the first 3 years and then decreases in the last 3 years. Our findings enhance our comprehension of the economic impact of air quality and make a valuable contribution to the nuanced understanding of willingness to pay for air quality, which is beneficial in assessing and optimizing environmental regulations. Keywords  Air pollution · Hedonic price · Instrumental variables · Heterogeneous effects · Dynamic impact · Willingness to pay * Martijn J. Smit m.j.smit@uu.nl Yuanyuan Cai yuancai@hku.hk Marco Helbich m.helbich@uu.nl 1 Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong 2 Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands ===== PAGE 14 (score 79, numeric-paren 50, table-words 6, var-words 9) ===== 898 Y. Cai et al. 1 3 difference showed that the coefficients of ­PM2.5 are significantly different over time, excluding between 2018 and 2019 (Table 11 in the Appendix). The coefficient for 2013 was − 0.00232 and statistically significant at the 1% level. Compared with 2013, the esti- mated coefficient decreased by − 0.00329 in 2014, by − 0.00341 in 2015, and by − 0.00217 in 2016, indicating that the negative effect of ­PM2.5 intensified over time. However, the estimated coefficient increased by 0.00078 in 2016, 0.000134 in 2017, and 0.00014 in 2019 (insignificant), relative to 2013, implying the ­PM2.5 negative effects reduced slightly over the last 3 years. Table 3   Regression results Heteroskedasticity-robust standard errors clustered at the community level. Standard errors in parentheses. Significance levels in all tables: *p < 0.1, **p < 0.05, ***p < 0.01 (1) (2) (4) OLS FE FE2sls PM2.5 − 0.000740*** − 0.000104*** − 0.000852*** (0.0000564) (0.0000183) (0.0000489) School 0.00109 − 0.000500 − 0.000619 (0.00152) (0.000621) (0.000639) Hospital 0.00259 0.00000956 − 0.0000938 (0.00193) (0.00149) (0.00153) Park − 0.00214 − 0.000844 − 0.000768 (0.00207) (0.00128) (0.00132) Shopping mall 0.00255 − 0.000897 − 0.000798 (0.00236) (0.00117) (0.00120) Farmer’s market 0.000310 0.000366 0.000478 (0.00185) (0.000826) (0.000850) Leisure facilities 0.0113*** − 0.00154* − 0.00136 (0.00195) (0.000812) (0.000834) Basic services 0.00384** 0.000859 0.000895 (0.00185) (0.000733) (0.000753) Population 0.190*** − 0.00397 − 0.00410 (0.00186) (0.00439) (0.00452) D_Bus stop 0.00146 − 0.00187* − 0.00200* (0.00195) (0.00108) (0.00111) D_Metro station 0.00111 − 0.000214 − 0.000120 (0.00239) (0.00229) (0.00235) D_CBD − 0.0162*** 0 0 (0.00241) (.) (.) Constant − 1.873*** 0.300*** 0.364*** (0.0410) (0.0491) (0.0506) Community effect # Yes Yes Year-fixed effect Yes Yes Yes N 33,538 33,538 33,538 Adjusted ­R2 0.458 0.839 0.830 ===== PAGE 15 (score 31, numeric-paren 18, table-words 2, var-words 8) ===== 899 Economic effects of air quality on housing prices: evidence… 1 3 Our results suggest that ­PM2.5 had a stronger influence on housing prices after the severe smog in 2013 (Mei et al., 2020), but that recently the effect has weakened to a minor extent. This may be due to air quality improvements across Beijing over time as a result of various environmental policies, such as the Clean Air Action Plan 2013–2017 (Zhang et al., 2016). In 2017, the concentration was < 60 µg/m3 and met the targets outlined in Bei- jing’s Clean Air Action Plan 2013–2017. 5  Conclusions Considering the endogeneity issues and potential heterogeneity effect across space–time, this study provides more precise and comprehensive evidence of the value of air quality on housing prices by taking thermal inversions as IVs. By using resale transaction prices of residential apartments in Beijing from 2013 to 2019, we evaluate the economic effect of air quality ­(PM2.5) as a whole and how its effects vary across regions, income groups, and over time. In keeping with most previous studies in both developed and developing countries (Chasco & Le Gallo, 2015; Hitaj et al., 2018; Tian et al., 2017), our results of Beijing confirm that ­PM2.5 is negatively associated with housing prices. We found that households are willing to pay 0.0852% of the housing unit price for a reduction of 1 µg/m3 in ­PM2.5 concentrations. This result indicates that the MWTP in Beijing is relatively moderate com- pared to that in developed countries (Bajari et al., 2012), and in other major Chinese cities such as Shanghai and Qingdao (Chen et al., 2018; Chen & Chen, 2012). Quantile regression with IVs showed the heterogenous effects of ­PM2.5 across income levels, high-income households are willing to pay more for clean air than low-income groups, echoing the results of prior studies (Chen et al., 2018). It seems that the rich care more about the health risks attributed to air pollution and thus tend to pay extra for good air quality, while monetary costs exclude the poor from this amenity since they have to spend their money on fixed costs and living essentials (Dong & Zeng, 2018). Based on our findings, it seems reasonable to tax high-income households to a larger degree when air pollution funds are set up. Policymakers should be aware that supporting households with low incomes might be a way to compensate for their lost welfare as a result of the spatial inequality of air quality in the city. Besides, spatially stratified models showed that Table 4   Heterogeneous effects: results of quantile regression with IVs Q25 Q50 Q75 Q90 PM2.5 − 0.0141*** − 0.0151*** − 0.0168*** − 0.0189*** (0.00039) (0.00033) (0.00028) (0.00031) Table 5   Heterogeneous effects: estimators based on the ring roads (FE2sls) Ring2 Ring3 Ring4 Ring5 Ring6 PM2.5 − 0.00111*** − 0.000867*** − 0.00104*** − 0.000774*** − 0.000622*** (0.000176) (0.0000932) (0.000106) (0.000124) (0.0000809) ===== PAGE 16 (score 27, numeric-paren 14, table-words 1, var-words 10) ===== 900 Y. Cai et al. 1 3 the significantly negative influence of ­PM2.5 is different across ring roads. In this case, envi- ronmental regulation should account for these regional differences rather than a ‘one size in all’ approach. Finally, we examined whether the ­PM2.5 effect varies temporally relative to the year 2013. We found that the negative impact gradually increased in 2014 and 2015; in 2016 it also increased but to a minor extent compared to the first 2 years. This could mean that the public became increasingly aware of health-threatening impacts owing to exposure to air pollutants in 2013. However, relative to 2013, the effect in 2017, 2018 and 2019 decreases. However, the difference did not reach statistical significance between 2018 and 2019. On the whole, the negative effect mainly declined in the last 3 years. This decline may be asso- ciated with pollution control actions like the Clean Air Action Plan 2013–2017. In fact, the measured concentration levels of ­PM2.5 in 2017 and 2019 met the targets of that action plan (Zhang et al., 2016). Hence, the dynamic effects also implicitly provide evidence for the effectiveness of Beijing’s environmental regulation actions. Further research is needed to determine whether it is the current reduction in pollution or future government commit- ments to improve air quality that impact the current willingness to pay. Our study had several limitations that must be considered when interpreting our find- ings. First, we incorporated only average quarterly ­PM2.5 concentrations as a proxy for air pollution rather than daily measurements. Though indirectly considered in our sensitiv- ity tests with the AQI, we cannot exclude those other pollutants (e.g., ­SO2, ­NO2, ­PM10) that may affect housing prices differently. More pollutant indicators could be considered in future work. Second, air pollutant data stemmed from a restricted number of official monitoring stations that are unevenly distributed across space. While our air pollution data facilitated the incorporation of spatiotemporal trends in ­PM2.5, small-scale variations are likely to have been unrecognized. Whether and, if so, to what extent this limitation affected our estimates needs to be addressed in the future using high-resolution air pollution data. Finally, because we used a hedonic model specification, we only indirectly evaluated the willingness to pay for clean air based on the housing market conditions, without obtaining people’s perceptions directly. Future research is advised to combine subjective and objec- tive measures to comprehensively explore the effect of air pollution. Appendix See Fig. 4 and Tables 7, 8, 9, 10 and 11. Table 6   Changes in the ­PM2.5 estimates compared with 2013 Year 2013 (Base- line) 2014 2015 2016 2017 2018 2019 PM2.5 − 0.00232*** − 0.00329*** − 0.00341*** − 0.00217*** 0.000780*** 0.000134** 0.000140** (0.000041) (0.000039) (0.000041) (0.00005) (0.000048) (0.00006) (0.000071) ===== PAGE 17 (score 8, numeric-paren 0, table-words 1, var-words 6) ===== 901 Economic effects of air quality on housing prices: evidence… 1 3 Fig. 4   Comparation between the regression coefficients of ­PM2.5 across ring roads Table 7   Overview of areas within each of Beijing’s ring roads Ring road Construction period Areas connected Functions of the area inside each ring Ring #1 1920s–1950s Boundary does not exist anymore; Tianan- men, Forbidden City, and Di’an men Historical areas Ring #2 1980s–1990s Dongcheng Qu (Eastern Urban Precinct), Xicheng Qu (Western Urban Precinct), Xuanwu Precinct and Chongwen Precinct Old city Ring #3 1980s–1990s Beijing’s CBD (Guandongdian) and diplo- matic communities (Dongzhimenwai/Liang- maqiao, Jianguomenwai) Central business district (CBD), as well as an important residential area for the local population Ring #4 Completed in 2001 Connects the Zhongguancun technology hub, western Beijing, the Fengtai District, and eastern Beijing Economic development zones, but also an important residential area for the local population Ring #5 Completed in 2003 Located approximately 10 km from central Beijing, and links the suburban areas of Huantie, Shigezhuang, Dingfuzhuang, etc. Also passes through the Beijing Develop- ment Area Residential area for immigrant population Ring #6 2001–09 Shunyi District, Tongzhou District, Chang- ping District, and Daxing District Suburban districts ===== PAGE 18 (score 43, numeric-paren 29, table-words 2, var-words 7) ===== 902 Y. Cai et al. 1 3 Table 8   Regression results of housing prices in the first stage Heteroskedasticity-robust standard errors clustered at community level, and standard errors in parentheses. Significance levels: *p < 0.1, **p < 0.05, ***p < 0.01 Variables Coefficients Variables Coefficients Floor area − 0.00179*** Floor1 − 0.0324*** (0.0000294) (0.00853) Age1 # Floor2 0.00618 # (0.00849) Age2 0.0431*** Floor3 0.0110 (0.00245) (0.00849) Age3 0.0492*** Floor4 0.00149 (0.00269) (0.00852) Age4 0.0631*** Floor5 0.0131 (0.00306) (0.00861) Age2 0.0336*** Bedroom number 0.0207*** (0.00445) (0.000884) Elevator − 0.0210*** Orientation 0.0220*** (0.00132) (0.000616) Dec1 0.0214*** Constant 10.86*** (0.00112) (0.00897) Dec2 − 0.00185 (0.00113) Dec3 − 0.0157*** (0.00185) Community effect Yes Quarter-fixed effect Yes N 333,269 Adjusted R2 0.925 ===== PAGE 19 (score 71, numeric-paren 51, table-words 2, var-words 9) ===== 903 Economic effects of air quality on housing prices: evidence… 1 3 Table 9   Robustness check for air quality: Regression estimates for AQI Heteroskedasticity-robust standard errors clustered at community level, and standard errors in parentheses. Significance levels: *p < 0.1, **p < 0.05, ***p < 0.01 (1) (2) (3) OLS FE FE2sls AQI − 0.000415*** − 0.000184*** − 0.00117*** (0.0000661) (0.0000221) (0.0000779) School 0.00111 − 0.000475 − 0.000552 (0.00152) (0.000621) (0.000659) Hospital 0.00262 0.0000264 − 0.000113 (0.00193) (0.00149) (0.00158) Park − 0.00224 − 0.000873 − 0.000770 (0.00208) (0.00128) (0.00136) Shopping mall 0.00244 − 0.000918 − 0.000873 (0.00237) (0.00117) (0.00124) Farmer’s market 0.000272 0.000334 0.000444 (0.00185) (0.000825) (0.000877) Leisure facilities 0.0112*** − 0.00157* − 0.00145* (0.00196) (0.000811) (0.000860) Basic services 0.00372** 0.000822 0.000985 (0.00185) (0.000732) (0.000776) Population 0.191*** − 0.00392 − 0.00387 (0.00187) (0.00439) (0.00466) D_Bus stop 0.00143 − 0.00182* − 0.00196* (0.00196) (0.00108) (0.00114) D_Metro station 0.00106 − 0.000220 − 0.000283 (0.00240) (0.00229) (0.00242) D_CBD − 0.0162*** 0 0 (0.00241) (.) (.) Constant − 1.956*** 0.268*** 0.435*** (0.0410) (0.0491) (0.0528) Community effect Yes Yes Yes Year-Fixed effect Yes Yes Yes N 33,538 33,538 33,538 R2 0.455 0.839 0.819 ===== PAGE 20 (score 90, numeric-paren 66, table-words 2, var-words 10) ===== 904 Y. Cai et al. 1 3 Table 10   Robustness check for air quality: Regression estimates for subsample within 3 km distance from any air monitoring station Standard errors in parentheses Significance levels: *p < 0.1, **p < 0.05, ***p < 0.01 (1) (2) (3) (4) OLS FE FE2sls PM2.5 − 0.000717*** − 0.0000975*** − 0.000832*** (0.0000853) (0.0000257) (0.0000729) AQI − 0.00117*** (0.000114) School − 0.00165 − 0.00180** − 0.00175* − 0.00172* (0.00230) (0.000885) (0.000911) (0.000939) Hospital 0.00256 − 0.000151 − 0.0000701 0.0000255 (0.00286) (0.00229) (0.00236) (0.00243) Park − 0.00305 − 0.000518 − 0.000300 − 0.000374 (0.00289) (0.00170) (0.00175) (0.00180) Shopping mall − 0.00260 − 0.00185 − 0.00171 − 0.00181 (0.00355) (0.00163) (0.00168) (0.00173) Farmer’s market − 0.00509* − 0.000803 − 0.000657 − 0.000750 (0.00267) (0.00115) (0.00119) (0.00123) Leisure facilities 0.00914*** − 0.00170 − 0.00172 − 0.00174 (0.00299) (0.00119) (0.00123) (0.00127) Basic services 0.0141*** 0.00156 0.00134 0.00149 (0.00280) (0.00107) (0.00110) (0.00113) Population 0.233*** 0.00111 0.0000220 0.00131 (0.00326) (0.00624) (0.00642) (0.00662) D_Bus stop − 0.00194 − 0.00205 − 0.00243 − 0.00232 (0.00301) (0.00154) (0.00158) (0.00163) D_Metro station 0.0333*** − 0.000692 − 0.000271 − 0.000280 (0.00361) (0.00311) (0.00319) (0.00329) D_CBD − 0.0287*** 0 0 0 (0.00362) (.) (.) (.) Constant − 2.276*** 0.370*** 0.439*** 0.504*** (0.0633) (0.0706) (0.0729) (0.0761) Community effect Yes Yes Yes Yes Year-fixed effect Yes Yes Yes Yes N 15,656 15,656 15,656 15,656 R2 0.450 0.852 0.843 0.833 ===== PAGE 21 (score 32, numeric-paren 20, table-words 1, var-words 8) ===== 905 Economic effects of air quality on housing prices: evidence… 1 3 Acknowledgements  We would like to thank Dr. Chong Liu for offering the raw data on air pollutants and helping calculate the indicators as instructive variables in our paper. Also, Prof. Carolina Castaldi, Prof. Pieter Hooimeijer, Dr. Jinlong Gao and Dr. Zhiqiang Hu are thanked for their helpful comments on earlier versions of this paper. Yuanyuan Cai would like to thank Prof. Carolina Castaldi again for her encourage- ment and coordination in this collaboration. The majority of her work on this article was conducted at UU. Author contributions  YC: Conceptualization, methodology, software, data curation, visualization, inves- tigation, writing—original draft preparation, review and editing. MS: Software, visualization, validation, review and editing. MH: Writing—review and editing. Declarations  Conflict of interest  The authors declare that they have no conflict of interest. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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