# RECORD cfc27a33426e6615c17a451873b0b54c65612252 # TITLE: Multilevel effects of urban form and urban functional zones on housing prices: evidence from open-source big data # YEAR: 2024.0 VENUE: Journal of Housing and the Built Environment DOI: 10.1007/s10901-023-10109-y # AUTHORS: Guan Huang; Si Qiao; A. Yeh # PAGES: 25 CANDIDATE TABLE PAGES (1-based): [11, 12, 16, 17, 21] # ABSTRACT: While the relationship between local housing prices and the urban form and distribution of urban functional zones in a single city is well-discussed, the conclusion is usually sensitive to a particular city context, and cross-city comparative study is limited. This study attempts to examine the influences of urban form and urban functional zone distribution on housing prices within and between cities after controlling the city-wide socio-economic and demographic differences. Based on multiple open-source big data, such as points-of-interest (POI) and historical housing transaction data, the hierarchical linear model is utilized to compare the housing market of 10 extra-large cities in China. Results indicate that the urban form and the urban functional zone distribution significantly influence housing prices after the socio-economic and demographic differences are controlled. For inter-city comparison, an urban form with high compactness, low centrality, low polycentricity, high density, and low dissimilarity in housing development is related to lower city-level housing prices. For intra-city, proximity to work centers, high-quality hospitals, and schools shows positive associations to housing prices. ===== PAGE 1 (score 11, numeric-paren 0, table-words 0, var-words 11) ===== Vol.:(0123456789) Journal of Housing and the Built Environment (2024) 39:987–1011 https://doi.org/10.1007/s10901-023-10109-y 1 3 ARTICLE Multilevel effects of urban form and urban functional zones on housing prices: evidence from open‑source big data Guan Huang1 · Si Qiao1   · Anthony Gar‑On Yeh1 Received: 1 November 2022 / Accepted: 22 December 2023 / Published online: 14 February 2024 © The Author(s) 2024 Abstract While the relationship between local housing prices and the urban form and distribution of urban functional zones in a single city is well-discussed, the conclusion is usually sensitive to a par- ticular city context, and cross-city comparative study is limited. This study attempts to examine the influences of urban form and urban functional zone distribution on housing prices within and between cities after controlling the city-wide socio-economic and demographic differences. Based on multiple open-source big data, such as points-of-interest (POI) and historical housing transaction data, the hierarchical linear model is utilized to compare the housing market of 10 extra-large cities in China. Results indicate that the urban form and the urban functional zone distribution significantly influence housing prices after the socio-economic and demographic differences are controlled. For inter-city comparison, an urban form with high compactness, low centrality, low polycentricity, high density, and low dissimilarity in housing development is related to lower city-level housing prices. For intra-city, proximity to work centers, high-quality hospitals, and schools shows positive associations to housing prices. Keywords  Urban form · Urban functional zone · Housing prices · Natural semantical language process · Multilevel effect · Open-source big data 1  Introduction Research on housing prices based on the hedonic model has widely clarified that the properties of houses (square footage, number of bedrooms, house orientation, building age, and so on), the location, and the neighborhood characteristics undoubtedly affect the prices (Goodman, 1978; Rosen, 1974). Typically, these empirical studies state them- selves as unique to the experience of a given city and attribute the uniqueness to the * Si Qiao siqiao@connect.hku.hk Guan Huang guanhuang@connect.hku.hk Anthony Gar‑On Yeh hdxugoy@hku.hk 1 Department of Urban Planning and Design, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China ===== PAGE 11 (score 9, numeric-paren 0, table-words 2, var-words 3) ===== 997 Multilevel effects of urban form and urban functional zones… 1 3 3.3  Potential determinants Several potential influencing factors possibly affect the HEPrice even after internalizing the housing structure attributes’ effects. According to previous literature reviews, we clas- sified three categories of indicators to evaluate the cross-scale potential determinants of housing estate price (see Table 3). Category 1 is the individual category, including the structure and location attributes of specific housing estates. Estate building year has a significant impact on housing price due to the bank loan policy in China. A high down payment is associated with an older house, a shorter load period, and a lower available loan amount. The increasing burden of down payment with the increased building year would intensely affect buyers’ choice and thus be reflected in the pricing strategy of sellers. Furthermore, the location attributes have a wide impact on the capability of accessing urban service opportunities and access to urban ser- vices may increase house values, as evidenced by higher prices for homes close to transit in previous studies (Duncan, 2011; Shen & Karimi, 2017). Table 2   Descriptive statistics of average per-square-meter housing estate price in the ten extra-large cities # : Price unit: yuan/m2, CNY. The currency exchange rate is 100 CNY to 15.65 USD for 22nd Oct 2021 City name Min Max Mean Std. Dev Counts Shanghai 10,629 170,319 6,5061.69 31,560.97 9,454 Beijing 17,937 172,414 6,5477.49 34,375.78 5,966 Shenzhen 24,166 131,000 6,1640.42 23,162.43 3,432 Guangzhou 7,879 93,759 3,5901.15 20,316.75 4,396 Chengdu 5,089 30,032 12,035.49 45,65.732 8,026 Hangzhou 9,915 100,799 39,198.38 19,098.93 3,310 Wuhan 5,918 32,654 16,397.68 55,36.613 4,098 Nanjing 9,418 83,836 34,011.27 15,025.99 3,749 Changsha 5,356 24,582 10,011.98 39,25.467 2,019 Xi’an 6,093 40,084 14,086.01 61,03.178 2,956 Fig. 4   Interpolated housing prices. (a) Beijing, (b) Xi’an, (c) Chengdu, (d) Shanghai, (e) Nanjing, (f) Hangzhou, (g) Wuhan, (h) Changsha, (i) Guangzhou, (j) Shenzhen ===== PAGE 12 (score 14, numeric-paren 0, table-words 2, var-words 8) ===== 998 G. Huang et al. 1 3 Table 3   Variables and statistics based on three categories of spatial scales Category Sub-category Variables [unit] Mean Std. dev Level 1. Housing estate indicators Estate structure attribute Building year [year] 1999.67 11.487 Level 1 Estate location attribute Proximity to metro station [kilometer] 1.57 2.008 Level 1 Proximity to bus station [kilometer] 0.27 0.136 Level 1 2. Intra-city indicators Estate-based urban functions attribute Proximity to working zone [kilometer] 0.53 0.386 Level 1 Proximity to shopping center [kilometer] 1.14 0.715 Level 1 Proximity to commercial business zone [kilometer] 2.74 2.276 Level 1 Proximity to park and green space [kilometer] 1.57 1.788 Level 1 Proximity to 3A hospital [kilometer] 5.00 5.462 Level 1 Proximity to primary and middle school [kilometer] 0.65 0.426 Level 1 3. Inter-city indicators City-based urban form attribute Compactness [index] 0.13 .06 Level 2 Centrality [index] 0.71 .116 Level 2 Polycentricity [index] 3.97 1.683 Level 2 Building area ratio [ratio] 1.47 .242 Level 2 Dissimilarity [index] 0.43 0.08 Level 2 City-based socio-economic attribute Gini coefficient of population [continuous] 0.51 .085 Level 2 Moran index of education [continuous] 0.20 .076 Level 2 GDP per capita [1 million Yuan/population] 0.18 .022 Level 2 ===== PAGE 13 (score 28, numeric-paren 0, table-words 7, var-words 14) ===== 999 Multilevel effects of urban form and urban functional zones… 1 3 Category 2 attempts to explore the effects of the distribution of urban functional zones in intra-city, which is an extension of the impact of housing estates on the neighborhood attributes. The proximity to urban functional zones represents the time cost and expense burden of reaching potential destinations to obtain urban services, such as medical service, educational resources, and physical exercise amenities. The proximity indicators of urban functional zones, including working zones, shopping centers, commercial business zones, parks and green spaces, 3A hospitals, and primary and middle schools, are selected as the level 1 indicators (Qiao et al., 2021; Tam et al., 2019; Wang et al., 2017; Wen et al., 2017; Zhang et al., 2023). Category 3 covers the city-level attributes of the socio-economic and physical urban form. The physical urban form indicators include the compactness, centrality, polycentric- ity, dissimilarity, and density. The details are presented in Sect. 2. The socio-economic fac- tors include the Gini coefficient of population, the Moran index of education, and the GDP per capita, (Tsai, 2005; Villar & Raya, 2015; Zhang et al., 2016). The Gini coefficient of population represents the unequal population distribution in the metropolitan area. A high Gini coefficient approaching 1 indicates a high unevenness of population. The Moran index of education ranges from -1 to 1, illustrating the clustering pattern for the low-educated population (educated in primary school or below). If the value approaches 0, then the pat- tern is randomly scattered, while an absolute value near 1 represents a clustered pattern. This indicator shows whether low education, as a proxy of poverty, is agglomerated. The GDP per capita indicates a city’s level of economic development. As the urban form indi- cator of building density is collinear with the population density, we omitted it. The final variables and statistics are shown in Table 3. The variance inflation factor (VIF) of the final variables is provided in Appendix Table A3. The VIFs of majority of variables are less than 5 and all VIFs are less than 10 indicating there is no significant multicollinearity between the final variables. 3.4  Hierarchical regression model The traditional ordinary least squares (OLS) hedonic model has been criticized for ignor- ing the existence of housing market segmentation (Goodman & Thibodeau, 1998, 2003). In short, a house occupies a unique location in space, and the geographic attributes tied to its location cannot be replicated. The demand for houses in certain locations may be very inelastic, such as houses near prestigious schools (Wen et al., 2017). The irreplaceability of different housing locations shows that houses are essentially nested in spatial submar- kets with different pricing premiums (Orford, 2000). Therefore, to solve the fixed effects of intra-city and inter-city submarkets, the hierarchical linear model (HLM) is introduced. In the first level, the individual housing estate attributes of proximity to urban functional zones are measured (see Table 3, “Level” column). This level reflects the housing values nested in intra-city functions. In the second level, the inter-city attributes of the urban phys- ical forms and the city-based socio-economics of the habitants are assessed. The formulas of the two-level HLM used in this study are as follows (Goodman & Thibodeau, 2003): where Pij is the logarithm-transformed HEPrice of estate i within city j; 훼pj (p = 0, 1, 2, …, p) is an intercept and regression coefficient. x1ij is the level 1 variable that represent indi- vidual housing estate attributes. eij is a random error. Level1 ∶Pij = 훼0j + 훼1j (x1ij ) + 훼2j (x2ij ) + ⋯+ 훼pj (xpij ) + eij, eij ∈N(0, 휎2), ===== PAGE 16 (score 65, numeric-paren 34, table-words 5, var-words 10) ===== 1002 G. Huang et al. 1 3 Table 5   Effects of urban forms and urban functional zones on housing estate prices at multi levels *** p < .01, **p < .05, *p < .1 Price (log) Model 1 Model 2 Model 3 OLS model Coef Robust Std.Err Coef Robust Std.Err Coef Robust Std.Err Coef Robust Std.Err Building year [level 1]  − 0.007*** 0.002  − 0.001 0.003  − 0.001 0.003 0.000** 0.000 Proximity to metro station [level 1]  − 0.008 0.007  − 0.008 0.007  − 0.010*** 0.002 Proximity to bus station [level 1] 0.105 0.070 0.105 0.070 0.114*** 0.015 Proximity to working zone [level 1]  − 0.092*** 0.033  − 0.092*** 0.033  − 0.091*** 0.005 Proximity to shopping center [level 1] 0.061*** 0.009 0.061*** 0.009 0.061*** 0.003 Proximity to commercial business zone [level 1]  − 0.012 0.009  − 0.012 0.009  − 0.016*** 0.001 Proximity to park and green space [level 1] 0.013 0.011 0.013 0.011 0.016*** 0.001 Proximity to 3A hospital [level 1]  − 0.044*** 0.003  − 0.044*** 0.003  − 0.042*** 0.001 Proximity to primary and middle school [level 1]  − 0.084** 0.032  − 0.084*** 0.032  − 0.088*** 0.005 Compactness [level 2]  − 10.996*** 0.327  − 10.822*** 0.054 Centrality [level 2] 1.287*** 0.225 1.436*** 0.037 Polycentricity [level 2] 0.180*** 0.054 0.189*** 0.007 Building density [level 2]  − 0.232*** 0.065  − 0.216*** 0.009 Dissimilarity [level 2] 1.458*** 0.425 1.682*** 0.057 Gini index of population [level 2]  − 1.375*** 0.227  − 1.457*** 0.037 Moran index of education [level 2]  − 0.718* 0.393  − 0.835*** 0.043 GDP per capita [level 2] 10.905*** 1.042 10.894*** 0.143 Level 1 Constant 24.585 5.998 11.849 5.633 10.901 5.494 10.193 0.420 Level 2 Constant 0.432 0.102 0.472 0.112 0.002 0.000 N.A N.A Level 2 Residual 0.199 0.024 0.131 0.012 0.131 0.012 N.A N.A Residual intra − class correlation 0.683 0.781 0.016 N.A Obs 47,316 47,316 47,316 47,316 Groups 10 10 10 N.A AIC 58,176.63 38,539.57 38,502.13 38,939.39 BIC 58,211.69 38,644.75 38,577.42 39,097.15 ===== PAGE 17 (score 19, numeric-paren 9, table-words 1, var-words 8) ===== 1003 Multilevel effects of urban form and urban functional zones… 1 3 In the comparison of Models 2 and 3 in Table 5, the residential intra-class correlation reveals that the inter-city differences explain 76.5% of the variance of HEPrice. This answers exactly how much of the variance of HEPrice is contributed by the city’s uniqueness. Model 3 also validates our third hypothesis that housing price variation is partially produced by differentiated urban forms and functions. Specifically, the HEPrice would decrease significantly (− 10.996***) with the increase of compactness, while centrality is positively associated with the housing price (1.287***). Polycentricity stimulates the city-level housing price (0.180***) because more working subcenters could attract migrants, thereby increasing the demands of residential settlements (Wen & Tao, 2015). The dissimilarity index is positively associated with the city’s HEPrice (1.458***), indicating that the uneven land development of the city is associated with high housing prices on the overall level. This result supplements the evidence of the impact of polycentricity on housing prices from another dimension. Balanced development was achieved through the allocation of state investment, which often drives the construction of new satellite cities/towns (Liu et al., 2018). The density index negatively affects the HEPrice (− 0.232***), suggesting that a high dwelling density is related to a low city-level housing price. This result is inconsistent with the previous intra-city evidence of housing premiums induced by local density (Kulish et al., 2012). High-density areas at the local level are often urban CBDs where the afflu- ent are attracted to live nearby, thereby increasing the housing prices. Conversely, for city-level density comparison, high urban density typically means a high level of house supply at the over- all level. When controlling other variables, such as the GDP and the population structure, a large amount of supply would depreciate the values of individual houses if the demand is fixed. In terms of urban functional zones, Model 3 shows that only urban functions of working zones (− 0.092***), 3A hospitals (− 0.044***), and primary and middle schools (− 0.084***) have premiums on housing prices after controlling the city’s differences. The findings are con- sistent with most of the previous empirical evidence that working opportunities, convenient com- muting, and valuable amenities, like hospitals and schools, have capitalization effects on housing prices (Wang et al., 2017; Wen et al., 2017). Conversely, proximity to shopping centers likely inhibits the surrounding housing values (0.061***). This result is consistent with that of a previ- ous study where large-scale and high-end shopping complexes have a significant negative impact on the housing prices in core urban areas (Zhang et al., 2020). 5  Conclusions Although previous studies have affirmed the contribution of a city’s uniqueness to the housing market in that city, this study supplements the empirical evidence of the influence of urban forms and urban functional zones on the differentiated performance of the housing market on multiple scales (within and between cities). Our findings indicate that the difference in housing prices across cities is not only attributable to the city’s uniqueness in the social and economic environ- ment (Mirkatouli et al., 2018; Waltert & Schläpfer, 2010; Wang et al., 2017; Yi & Huang, 2014) but also to the urban form, which is led by urban growth strategies. For the different urban forms in the five dimensions, our results show that land use development policies for low compactness, high centrality, high polycentricity, low density, and high dissimilarity in housing development may significantly increase the average hous- ing price of the city’s housing market. This phenomenon indicates that paying attention to the housing unaffordability within the cities due to the influences of house location should also regulate the housing market on the city level through appropriate urban planning and design. For example, when considering developing a new urban sub-center, constructing ===== PAGE 18 (score 26, numeric-paren 0, table-words 1, var-words 24) ===== 1004 G. Huang et al. 1 3 high-density communities could provide more houses for migrants and reduce the housing prices (Fesselmeyer & Seah, 2018). Our research proposes to examine the impacts of the compactness, centrality, polycentricity, and dissimilarity in housing development and the density on housing prices in the evaluation of urban development projects. For the distribution of urban functional zones within the cities, housing prices are affected by the spatial distribution of urban functions is well studied in previous studies (Qiao et al., 2021; Tam et al., 2019; Wang et al., 2017; Wen et al., 2017). But our finding provides a unified per- spective for such single-city housing price studies (Shen & Karimi, 2017). After controlling the city as an influencing factor of housing price differences, working centers, high-quality hospitals, and schools can increase the housing prices of neighboring areas. This research has its limitations. The spatial form of a city is always more or less related to its level of economic development. The world’s most economically developed global cit- ies (e.g., Chicago, Tokyo, Hong Kong, New York, Shanghai) are high-density cities with high housing prices. We cannot rashly infer that if a city is actively designed and built into a certain type of form, it will automatically push up or decrease local housing prices. The focus of this research is to answer whether the design and planning of the city’s physical facilities and urban functional zones’ layouts can certainly affect the housing prices if the social and economic conditions are equivalent to other cities. The findings emphasize that the moderating role of urban design and planning in social inequality caused by the hous- ing unaffordability is feasible and our conclusions highlight a series of urban design indica- tors as a toolbox for planners and policymakers. Appendix See Tables 6, 7, 8, 9. Table 6   POI categorizations of Gaode Map used in this study Top type (13 categories, all) Middle type (119 catego- ries, examples) Bottom type (2168 categories, examples) Accommodation service Hotel, inn Economical hotel, motel, hostel Commercial housing Residential building, industrial park Mansion, apartment, creative industry center Daily life service Ticket office, express Train ticket office, barber shop, express Educational and cultural service School, museum University, high school, science museum Financial and insurance service Bank, insurance Bank of China, China Pacific Insurance Company, ATM Firms and enterprises Company, factory Telecom company, medical company, con- struction company Food and beverage Chinese food, fast food Sichuan food, fast food, sea food Government agencies and social organizations Social organization, government organization Charity, court, city hall Health care service Hospital, clinic General hospital, clinic, drug store Shopping service Supermarket, mall Wal-Mart, book store, clothing store Sports and recreation Sports, recreation Fitness center, cinema, playground Tourist attraction Scenic spot, park and square Park, temple, botanical garden Transportation facility Airport, train station Airport site, train station, subway station ===== PAGE 21 (score 56, numeric-paren 44, table-words 1, var-words 1) ===== 1007 Multilevel effects of urban form and urban functional zones… 1 3 Table 9   Fisher-Hayter pairwise comparisons of HEPrice for ten study cities City vs. City City means# Mean diff FH-test Beijing vs. Guangzhou 65.5 35.9 29.57 96.3627* Beijing vs. Shanghai 65.5 65.1 0.42 1.6286 Beijing vs. Nanjing 65.5 34.0 31.47 97.7773* Beijing vs. Wuhan 65.5 16.4 49.08 156.6610* Beijing vs. Chengdu 65.5 12.0 53.44 202.4649* Beijing vs. Xi’an 65.5 14.1 51.39 147.9686* Beijing vs. Hangzhou 65.5 39.2 26.28 78.5239* Beijing vs. Changsha 65.5 10.0 55.47 139.5118* Beijing vs. Shenzhen 65.5 61.6 3.84 11.5009* Guangzhou vs. Shanghai 35.9 65.1 29.16 103.4482* Guangzhou vs. Nanjing 35.9 34.0 1.89 5.5054* Guangzhou vs. Wuhan 35.9 16.4 19.50 58.1683* Guangzhou vs. Chengdu 35.9 12.0 23.87 82.3705* Guangzhou vs. Xi’an 35.9 14.1 21.82 59.3953* Guangzhou vs. Hangzhou 35.9 39.2 3.30 9.2788* Guangzhou vs. Changsha 35.9 10.0 25.89 62.3638* Guangzhou vs. Shenzhen 35.9 61.6 25.74 72.6323* Shanghai vs. Nanjing 65.1 34.0 31.05 104.1867* Shanghai vs. Wuhan 65.1 16.4 48.66 168.5061* Shanghai vs. Chengdu 65.1 12.0 53.03 226.2522* Shanghai vs. Xi’an 65.1 14.1 50.98 156.6581* Shanghai vs. Hangzhou 65.1 39.2 25.86 82.9332* Shanghai vs. Changsha 65.1 10.0 55.05 145.4147* Shanghai vs. Shenzhen 65.1 61.6 3.42 11.0098* Nanjing vs. Wuhan 34.0 16.4 17.61 50.4725* Nanjing vs. Chengdu 34.0 12.0 21.98 71.9427* Nanjing vs. Xi’an 34.0 14.1 19.93 52.4602* Nanjing vs. Hangzhou 34.0 39.2 5.19 14.0845* Nanjing vs. Changsha 34.0 10.0 24.00 56.3024* Nanjing vs. Shenzhen 34.0 61.6 27.63 75.2126* Wuhan vs. Chengdu 16.4 12.0 4.36 14.7141* Wuhan vs. Xi’an 16.4 14.1 2.31 6.2039* Wuhan vs. Hangzhou 16.4 39.2 22.80 63.1849* Wuhan vs. Changsha 16.4 10.0 6.39 15.2093* Wuhan vs. Shenzhen 16.4 61.6 45.24 125.7092* Chengdu vs. Xi’an 12.0 14.1 2.05 6.1722* Chengdu vs. Hangzhou 12.0 39.2 27.16 85.1580* Chengdu vs. Changsha 12.0 10.0 2.02 5.2634* Chengdu vs. Shenzhen 12.0 61.6 49.60 156.0455* Xi’an vs. Hangzhou 14.1 39.2 25.11 64.2650* Xi’an vs. Changsha 14.1 10.0 4.07 9.1383* Xi’an vs. Shenzhen 14.1 61.6 47.55 121.9721* Hangzhou vs. Changsha 39.2 10.0 29.19 66.9353* Hangzhou vs. Shenzhen 39.2 61.6 22.44 59.2678* Changsha vs. Shenzhen 10.0 61.6 51.63 118.6187* ===== PAGE 22 (score 8, numeric-paren 0, table-words 1, var-words 6) ===== 1008 G. Huang et al. 1 3 Acknowledgements  This research project is funded by the National Key R&D Program of China (2019YFB1600703). 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 Com- mons 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. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Adnan, M., Longley, P. A., Singleton, A. D., & Brunsdon, C. (2010). 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Table 9   (continued) Studentized range critical value (0.05, 9, 47,306) equals to 4.3865448 #  the unit of price is 1,000 CNY * Means the difference is significant between two cities