# RECORD bd42cea5779904b39c488f5e59235573696a2827 # TITLE: THE IMPACT OF URBAN RENEWAL ON NEIGHBORING HOUSING PRICES: AN APPLICATION OF HIERARCHICAL LINEAR MODELING # YEAR: 2022.0 VENUE: International Journal of Strategic Property Management DOI: 10.3846/ijspm.2022.15971 # AUTHORS: Chun-Chang Lee; Chih-Min Liang; Wen-Chih Yeh; Zheng Yu # PAGES: 13 CANDIDATE TABLE PAGES (1-based): [9, 10] # ABSTRACT: This study explored the impacts of urban renewal projects on neighboring housing prices. Hierarchical linear modeling (HLM) was employed to analyze urban renewal projects in Taipei City. The Level 1 independent variables pertained to a house itself (19,157 pieces of data), such as its structure and neighborhood attributes. The Level 2 variable pertained to an urban renewal project (23 cases of urban renewal), and the explanatory variable was the scale of each urban renewal project. The study examined whether differences exist between the impacts of various urban renewal projects on neighboring housing prices, and analyzed the extent to which the differences in neighboring housing prices are caused by the differences between urban renewal projects. The empirical results showed that the mean housing price varies significantly between each urban renewal project. In regard to the variance in the mean house price, 31.46% was caused by the differences between the urban renewal projects. The estimated coefficient of the grand floor area of urban renewal (FLAREA) had a positive value and attained a 1% level of significance. This indicates that the larger the scale of an urban renewal project, the larger its effects on neighboring housing prices. The empirical results of this study could better explain the impacts of the scale of an urban renewal project on the externalities of urban renewal. ===== PAGE 1 (score 14, numeric-paren 1, table-words 1, var-words 11) ===== Copyright © 2022 The Author(s). Published by Vilnius Gediminas Technical University This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unre- stricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Introduction According to Article 4 of Taiwan’s Urban Renewal Act, the primary objectives of urban renewal are to promote comprehensive environmental sustainability, improve the quality of living, and transform the appearance and aes- thetics of a city. The process of urban renewal entails three approaches: reconstruction, renovation, and maintenance. In reconstruction, specific urban renewal areas are desig- nated where the existence of worn-out houses could po- tentially impact their surrounding area, and these build- ings are demolished on a large scale in order to enhance the quality of living and to promote the growth of hous- ing prices in neighboring areas. In renovation and main- tenance, houses that have a dilapidated exterior, a visually unappealing appearance, or a deteriorated functionality are subjected to small-scale renovations and facelifts to enhance their functionality and usage as well as to beau- tify the city they are found in. With regard to the issue of urban renewal on housing prices, many studies have explored the impacts of urban renewal laws and provisions and analyzed the imple- mentation of urban renewal projects. For instance, Jones (2008) and Hyra (2008) examined urban renewal and the post-war “new urban renewal” in the United States. Both authors focused on the racial differences and expounded unjust urban renewal projects by attaching importance to the process of community participation and the continu- ous development of housing prices. Some studies and data reviewers had analyzed the impacts of an urban renewal project on housing prices within the project area after its completion, and few had studied the impacts of urban re- newal and reconstruction on housing prices in neighbor- ing areas. In a recent study, Lee et al. (2017) investigated the impacts of urban renewal delineation time on housing prices in neighboring areas by employing the difference- in-difference estimation approach. The empirical results International Journal of Strategic Property Management ISSN: 1648-715X / eISSN: 1648-9179 2022 Volume 26 Issue 1: 11–23 https://doi.org/10.3846/ijspm.2022.15971 *Corresponding author. E-mail: lcc@mail.nptu.edu.tw THE IMPACT OF URBAN RENEWAL ON NEIGHBORING HOUSING PRICES: AN APPLICATION OF HIERARCHICAL LINEAR MODELING Chun-Chang LEE1,*, Chih-Min LIANG2, Wen-Chih YEH3, Zheng YU4 1 Department of Real Estate Management, National Pingtung University, Pingtung, Taiwan 2 Department of Public Finance and Tax Administration, National Taipei University of Business, Taipei, Taiwan 3 Department of Real Estate Management, HungKuo Delin University of Technology, New Taipei, Taiwan 4 Department of Land Economics, National Chengchi University, Taipei, Taiwan Received 24 May 2021; accepted 10 September 2021; first published online 17 January 2022 Abstract. This study explored the impacts of urban renewal projects on neighboring housing prices. Hierarchical linear modeling (HLM) was employed to analyze urban renewal projects in Taipei City. The Level 1 independent variables per- tained to a house itself (19,157 pieces of data), such as its structure and neighborhood attributes. The Level 2 variable pertained to an urban renewal project (23 cases of urban renewal), and the explanatory variable was the scale of each urban renewal project. The study examined whether differences exist between the impacts of various urban renewal pro- jects on neighboring housing prices, and analyzed the extent to which the differences in neighboring housing prices are caused by the differences between urban renewal projects. The empirical results showed that the mean housing price varies significantly between each urban renewal project. In regard to the variance in the mean house price, 31.46% was caused by the differences between the urban renewal projects. The estimated coefficient of the grand floor area of urban renewal (FLAREA) had a positive value and attained a 1% level of significance. This indicates that the larger the scale of an urban renewal project, the larger its effects on neighboring housing prices. The empirical results of this study could better explain the impacts of the scale of an urban renewal project on the externalities of urban renewal. Keywords: urban renewal, housing prices, grand floor area, scale of an urban renewal project, hierarchical linear modelling. ===== PAGE 9 (score 33, numeric-paren 2, table-words 5, var-words 14) ===== International Journal of Strategic Property Management, 2022, 26(1): 11–23 19 on urban renewal, in order to prevent overextension of the distance to affect the estimation. Therefore, we narrowed the range of effects to within 800 meters. On this basis, 800 meters served as the comparison benchmark of the range of influence in this study, and data of houses located outside the range of influence was removed. To measure the actual distance from a house to pub- lic infrastructure, this study utilized a geographic infor- mation system software – Arc GIS version 10.1 to link the data of a house’s internal and external characteristics, while integrating the spatial location data of the house and the sample image layer data to construct a database on a house’s spatial attributes. This study obtained the address- es of transacted houses from the Taiwan Real Estate Trans- action Bulletin. Afterwards, Google Maps and the Taiwan Geospatial One-Stop Portal (TGOS) were employed to convert all sets of house transaction data into spatial data in accordance with the addresses of transacted houses ob- tained from the Taiwan Real Estate Transaction Bulletin. Afterwards, the location of the sample points of the house transaction spatial data was established in the image layer of Taipei City’s administrative districts. After constructing each set of spatial data on housing transactions within the scope of research, the geographic information system soft- ware Arc GIS version 10.1 developed by Environmental Systems Research Institute, Inc. (ESRI) was used to overlay the layer of the location of the house transaction spatial data on other layers of relevant public infrastructure, such as MRT stations, railway stations, and schools, with the purpose of calculating the nearest distance from each spa- tialized housing data to public infrastructure. 4. Descriptive statistics of samples The descriptive statistics of the sample is presented in Ta- ble 2. The mean housing price was NT$26.6933 million. (1 USD = 27.9 NTD, January 6, 2021) The mean housing area was 43.28 ping. The mean house age was 21.90 years. The mean number of rooms was 2.92. The mean number of living rooms was 1.85. The mean number of bathrooms was 1.74. 7% of houses were located on the first floor. In terms of the type of housing, 44%, 30%, and 26% of houses were high residential buildings, low residential buildings, and apartment buildings, respectively. 37% of houses had a parking lot(s). The mean distance to the city center was 598.68 m. The mean distance to the nearest elementary school was 365.74 m. The mean distance to the nearest junior high school was 503.63 m. The mean distance to the nearest MRT station was 632.41 m. The mean distance to the nearest railway station was 3276.43 m. The mean grand floor area of urban renewal was 5404.24 m2. 5. Empirical results First, we used the null model to test the variation in the mean house price between various urban renewal projects. As shown in Table 3, with regard to fixed effects, the es- timated value of the mean house price g00 is 12.095; with regard to random effects, the estimate value of t00 is 0.028, and the statistics of c2 is 8861.028, the degree of freedom is 22 (J = 23 projects), attaining a 1% level of significance. These results show that the mean housing price varies sig- nificantly between each urban renewal project. The variance t00 of the mean urban renewal project price b0j and the individual level intra-group variance s2 can be used to calculate the intra-group correlation coef- ficient (ICC), which is 0.3146 (0.028 / (0.028 + 0.061) = 0.3146). According to Cohen’s (1998) recommendations, the correlation is strong. This indicates that 31.46% of the variance in the mean house price is caused by the differ- ences between the urban renewal projects. Thus, it is im- portant to take into account the differences between urban renewal projects. Table 2. Descriptive statistics of the sample Variable Mean S.D. Minimum Maximum Level 1 PRICE (in NT$10,000 units) 2669.33 1676.64 33.50 24500.00 AREA (ping) 43.28 20.07 20.00 149.83 AGE (years) 21.90 13.47 1.00 49.98 ROOM 2.92 0.83 1 6 LIVROOM 1.85 0.40 1 3 BATH 1.74 0.59 1 5 FLOOR1 0.07 – 0 1 BUILDING 0.44 – 0 1 HUAXIA 0.30 – 0 1 PARKING 0.37 0 1 DISTURBA 598.68 261.40 0 999.99 PRIM 365.74 173.16 0 1114.77 JUNI 503.63 255.85 3.36 1436.33 MRT 632.41 482.35 3.70 2388.18 TRAIN 3276.43 2474.58 68.96 8842.66 Level 2 FLAREA 5404.24 4274.29 906.62 17669.32 Table 3. Null model analysis results Fixed effects Coefficients Standardi­ zed errors t-ratio p-value g00 12.095*** 0.035 344.386 0.001 Random effects Variance components df Chi- square p-value t00 0.028*** 22 8861.028 0.001 Level-1 s2 0.061 Deviance (-2LL) 933.978 Number of estimated parameters 2 Note: * indicates p < 0.1, ** indicates p < 0.05, *** indicates p < 0.01. ===== PAGE 10 (score 75, numeric-paren 17, table-words 14, var-words 24) ===== 20 C.-C. Lee et al. The impact of urban renewal on neighboring housing prices: an application... The empirical estimation results are presented in Ta- ble 4. The estimated coefficient of the grand floor area of urban renewal (FLAREA) was 0.001 and attained a 1% level of significance. This indicates that the large the scale of an urban renewal project, the larger its effects on neigh- boring housing prices. Guidry et al. (1999) explored the effects of urban residential land size on residential land prices. The results demonstrated that lot size had a posi- tive effect on land price, albeit not statistically significant. With regard to the housing structure attributes, the sym- bols of most of the variables were as expected. The es- timated coefficient of house area was 0.001 and attained a 1% level of significance. This indicates that the larger the house area, the higher the price. Freybote and Fruits (2015) demonstrated that house area has a significant and positive effect on housing prices. The estimated coefficient of building age and the square of building age was –0.012 and 0.001, respectively, and both coefficients attained a 1% level of significance. This indicates that the higher the building age, the lower the price, and the smaller the price drop. Wilhelmsson (2008) also employed house age and the square of house age as explanatory variables, and showed the negative impacts of house age, in which the impacts decrease with increasing house age. The estimated coefficient of the number of living rooms was 0.024, and attained a 1% level of significance. This indicates that the higher the number of living rooms, the higher the hous- ing price. Huang et al. (2010) stated that a living room is an internal layout of a house and the more living rooms a house has, the higher its floor space as well as its price. They expected that the number of living rooms have a positive impact on housing price as well. The estimated coefficient of the number of bathrooms was 0.049, and attained a 1% level of significance. This indicates that the higher the number of bathrooms, the higher the hous- ing price. Baltagi and Bresson (2011) demonstrated the positive influence of the number of bathrooms on housing price. The estimated coefficient of floor number was 0.167, and attained a 1% level of significance. This indicates that the mean price of houses on the first floor was 16.7% high- er than those that are not on the first floor. Houses on the first floor can also be used for commercial purposes, which explains why they often have higher prices. The es- timated coefficients of high residential buildings and low residential buildings was 0.197 and 0.174, respectively, and coefficients attained a 1% level of significance. This indicates that the mean price of high residential buildings and low residential buildings was 19.7% and 17.4% high- er than apartment buildings, respectively. Since high and low residential buildings have many floors, due to safety concerns, housing prices would increase in response to higher construction costs. The estimated coefficient of the number of rooms was –0.016, and attained a 1% level of significance. The estimated coefficient of the availability of parking lots was –0.065 and attained a 1% level of signifi- cance. Christiansen et al. (2017) highlighted that houses with parking lots would pose a higher challenge to afford- able housing. This also shows that for house buyers who purchased low- to mid-priced houses, buying a house with a parking lot increases affordability, and they would rather buy houses located near a MRT station or those with a larger floor space. With regard to the neighborhood environment attrib- utes, the estimated coefficient of the distance to the city center –0.001 and attained a 1% level of significance. This indicates that the closer a house is to the city center, the higher the price. Sunak and Madlener (2016) pointed out that the city center offers more infrastructure, is more developed, and has more commercial and business ben- efits. Therefore, the closer a house is to the city center, the higher its price. The coefficient of the distance to the nearest elementary school (PRIM) was 0.001 and attained a 1% level of significance. The coefficient of the distance to the nearest junior high school (JUNI) was –0.001 and attained a 10% level of significance. This indicates that a junior high school offers school district-related benefits to houses nearby. Proximity to a school not only ensures the accessibility and safety of school children, but also increas- es recreational space for residents. Therefore, house buyers often consider whether a house is located near a school when they make their purchases. The estimated coefficient of the distance to the nearest MRT station was –0.001 and attained a 1% level of significance. This indicates that the closer a house is to a MRT station, the higher the price. Many middle-to-low income groups rely on public trans- portation to commute or travel, and hence a house that is located nearer to a MRT station is often more expensive (Ahlfeldt & Maennig, 2010). The estimated coefficient of the distance to the nearest railway station (TRAIN) was 0.001, and attained the 1% level of significance. Nelson Table 4. Empirical estimation results Fixed effects Coefficients Standardized errors t-ratio g00 11.960*** 0.005 2203.893 FLAREA g01 0.001*** 0.001 8.803 AREA g10 0.001*** 0.001 6.831 AGE g20 –0.012*** 0.001 –18.253 AGES g30 0.0002*** 0.001 18.743 ROOM g40 –0.016*** 0.003 –5.233 LIVROOM g50 0.024*** 0.005 4.312 BATH g60 0.049*** 0.005 10.971 FLOOR1 g70 0.167*** 0.008 20.975 BUILDING g80 0.197*** 0.007 29.409 HUAXIA g90 0.174*** 0.006 28.168 PARKING g100 –0.065*** 0.006 –10.403 DISURBA g110 –0.001*** 0.001 –6.099 PRIM g120 0.001*** 0.001 10.814 JUNI g130 –0.001* 0.001 –1.775 MRT g140 –0.001*** 0.001 –39.312 TRAIN g150 0.001*** 0.001 5.638 Note: * indicates p < 0.1, ** indicates p < 0.05, *** indicates p < 0.01. ===== PAGE 11 (score 11, numeric-paren 0, table-words 1, var-words 9) ===== International Journal of Strategic Property Management, 2022, 26(1): 11–23 21 (2004) and Simons and Jaouhari (2004) concurred that while proximity to a railway station offers ease of transpor- tation, a house that is too close to a railway station would be exposed to high noise levels and a high frequency of accidents, which has negative impacts on its price. Comber and Arribas-Bel (2017) pointed out that crime rates could be higher in places near railway stations. Conclusions and recommendations Conventional hedonic pricing models generally use the ordinary least squares (OLS) estimation approach, spatial econometrics or other methods, in which data with dif- ferent hierarchical characteristics are confined to a single level for data processing, and the estimation of hierarchi- cal problems are not taken into account. Since housing units are nested within various urban renewal projects, the impacts of an urban renewal project on neighboring housing prices should take into account the intrinsic dif- ferences of the project on neighboring housing prices. Hi- erarchical linear modeling (HLM) was employed for data analysis. Compared to the conventional ordinary least squares (OLS) models, HLM can further provide deeper insights into the relationships between housing prices and the characteristics of urban renewal projects. Arribas et al. (2016) think that OLS couldn’t solve the within-group correlation of datasets on housing prices or the correlation between housing prices and neighborhood environment. Housing information belongs to the nested structure. If we use single-level OLS, it will lead to infer- ence errors. In past research, the paper by Lee et al. (2017) and Liang et al. (2020) adopted HLM model to study the effects of urban renewal on housing prices, and Liang et al. (2020) adopted spatial econometrics for their research. They both neglected the hierarchical structure of hous- ing information. This will generate bias in the modeling estimation. In addition, it will not be possible to observe the cross-level influence of the effects of urban renewal. In this study, the HLM we adopted considered hierarchical structures, which can provide more information for the estimation and the result will be more accurate (Arribas et al., 2016). By using HLM analysis, we can understand how variations in individual houses affect Level 1 and how (the explained variation) urban renewal affects Level 2. The empirical results showed that the mean housing price varies significantly between each urban renewal project. 31.46% of the variance in the mean house price is caused by the differences between the urban renewal proj- ects. In this regard, it is important to take into account the differences between urban renewal projects when examin- ing the impacts of urban renewal projects on neighboring housing prices. The estimated coefficient of the grand floor area of urban renewal (FLAREA) had a positive value and attained a 1% level of significance. This indicates that the larger the scale of an urban renewal project, the larger its effects on neighboring housing prices. Our findings show that urban renewal projects of different scales have differ- ent impacts on neighboring housing prices. Previously, we also used 500 meters as the range of effects for estimation, but there was no significant difference in the results. Subsequent research could include other features of urban renewal for analysis to allow for a more thorough analysis on the impacts of urban renewal on neighbor- ing housing prices. In addition, we suggest examining whether the features of urban renewal projects moderate the impacts of housing features on housing prices, that is, whether moderating effects exist between the aggregate- level variables and the individual-level variables. Level 1 of this research is the attributes of the neigh- borhood environment of the individual house (such as the distances to downtown, to schools, to the nearest MRT station, to the nearest train station). This means that the attributes of the neighborhood environment are variables of Level 1. If thinking about taking “urban renewal proj- ects are nested within various neighborhood areas” for de- sign, it will set the neighborhood environment as the third level of variables. If you are thinking about three levels of analysis, it means the individual house is nested in cases of urban renewal, and cases of urban renewal are nested in the neighborhood. Now we must deal with the problem of measuring the neighborhood–how to divide the neigh- borhood–which was unclear in Taiwan. This time the variables that represent the neighborhood environment of each neighborhood are the distance of the neighbor- hood to downtown, to schools, to the nearest MRT sta- tion, to the nearest train station. If these are the variables of the neighborhood environment, it creates the problem of overlapping variables of the neighborhood environment from Level 1. Analysis on three levels is complicated, and for Level 3 we must consider how to represent the char- acteristic variables of Level 3. This analysis will be more complicated than ever. However, if we consider taking Level 1 as the individual house, Level 2 as the cases of urban renewal, and Level 3 as the administrative area, this is more feasible, and we suggest this should also be the direction of future research. Acknowledgements Thanks to Miss Dai-juan Liu for helping to draw Figure 1. References Ahlfeldt, G. M., & Maennig, W. (2010). Impact of sports arenas on land values: evidence from Berlin. The Annals of Regional Sci- ence, 44(2), 205–227. https://doi.org/10.1007/s00168-008-0249-4 Arribas, I., García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2016). Mass appraisal of residential real estate using mul- tilevel modelling. International Journal of Strategic Property Management, 20(1), 77–87. https://doi.org/10.3846/1648715X.2015.1134702 Baltagi, B. H., & Bresson, G. (2011). Maximum likelihood es- timation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: an application to hedonic housing prices in Paris. Journal of Ur- ban Economics, 69(1), 24–42. https://doi.org/10.1016/j.jue.2010.08.007