# RECORD 37a309783a11809263623703621db3a26e46597f # TITLE: The Effect of Central Business District on House Prices in Chengdu Metropolitan Area: A Hedonic Approach # YEAR: 2015.0 VENUE: nan DOI: 10.2991/cas-15.2015.83 # AUTHORS: Gao-Lu Zou # PAGES: 4 CANDIDATE TABLE PAGES (1-based): [2, 3] # ABSTRACT: Gaolu Zou Chengdu Economic and Development Institute of Conventions and Exhibitions, Chengdu University, Chengdu 610106, Sichuan Province, China Abstract—This paper aims to analyse the effects of the CBD and several selected housing features on house prices in Chengdu. Sampling is focused on ordinary old housing units. Data were collected from metropolitan Chengdu, China. Utilizing log-linear hedonic techniques, the study suggests a small negative price gradient from the CBD and a marginal and nonlinear effect of housing age and underground railway locations on house prices. Therefore, the study supports the classic inference for spatial house prices. Additionally, we suggest higher property tax rates for large-floor space units. ===== PAGE 1 (score 29, numeric-paren 0, table-words 2, var-words 25) ===== The Effect of Central Business District on House Prices in Chengdu Metropolitan Area: A Hedonic Approach Gaolu Zou Chengdu Economic and Development Institute of Conventions and Exhibitions, Chengdu University, Chengdu 610106, Sichuan Province, China     Abstract—This paper aims to analyse the effects of the CBD and several selected housing features on house prices in Chengdu. Sampling is focused on ordinary old housing units. Data were collected from metropolitan Chengdu, China. Utilizing log-linear hedonic techniques, the study suggests a small negative price gradient from the CBD and a marginal and nonlinear effect of housing age and underground railway locations on house prices. Therefore, the study supports the classic inference for spatial house prices. Additionally, we suggest higher property tax rates for large-floor space units. Keywords-central business district; housing feature; price, hedonic; log-linear; marginal effect. I. INTRODUCTION Chengdu metropolis displays a spatially concentric pattern. Classic urban housing market modeling starts from a monocentric city with an only business or employment center (the Central Business District known as CBD), e.g. [1, 2]. House prices vary inversely with distance or travel time to the CBD, holding other amenities constant. This is because the housing spatial equilibrium requires a negative price gradient. However, empirical evidence for the negative price gradient is inconsistent[3]. For a long period, the CBD did not affect house prices. However, house values now decline by more than 8% per mile (from the CBD)[4]. The CBD in Chengdu is situated in Yan Shi Kou nexus. Starting from CBD outwards are four ring expressways, i.e., Ring Road No. 1, Ring Road No. 2, Ring Road No. 3 and Around-the-city Expressway. Renming Nan Lu Road and Shu Du Da Dao Avenue are two main radial avenues starting from CBD outwards. Thus, nonlinear and concave urban property prices may exist in Chengdu metropolitan area[5, 6]. House prices have a tendency to change marginally with various features such as the distance and age[7]. Chengdu has since 2008 conducted large-scale development activities on underground railways. The dual effects of urban transit tracks on real estate prices are suggested[8-10]. Elasticity of private house price relative to walking distance is -0.02[11]. Railway stations[12], light rails[13], underground stations[5] increased either land or house values. However, negative externalities such as noise and congestion reduced house prices[8, 14]. This paper mainly aims to examine the nonlinear effects of the CBD and several selected housing featureson house prices in metropolitan Chengdu. We introduced a hedonic technique and thus estimated implicit house prices. We expect that the paper would contribute to the house price gradient from the CBD, property valuation and property tax policy. II. METHODOLOGY Housing properties are composed of various features[14, 15]. Hedonic methods could discover the prices of these housing features[16]. Hedonic prices are often called implicit prices because they are not explicitly ‘viewed’ or listed on board. [17] suggested the alternative functional forms of hedonic models. Past studies on real estate markets widely use hedonic techniques, e.g. [14, 18-28]. One commonly used hedonic technique is the log-linear equation[29-31]. Right inferences could be reached using the log-linear form[32]. It is formulated as ε β α + + + = ∑ ∑ = = n j j i m i i D c X PRICE 1 j 1 ln ln Where PRICE is the house price. Xi represents the ith feature of m housing features. Dj represents the jth feature of n housing features. D stands for dummy variable, e.g. CBD location and lake view. Ln denotes the natural logarithm. εis an error term. βi is constant elasticity given continual variable Xi, or implicit price Pi= i i β X given integer variable Xi. Existence of Dj implies that price change is % 100 ) 1 ( × − j ce [33]. Marginal house price changes could be suggested using estimates of the coefficients[7]. III. DATA AND VARIABLE DEFINITION We selected 180 ordinary-type older units in Chengdu metropolitan area. Sampling area is bounded within the Around-the-city Expressway. Suppose that eleven housing features impact house price PRICE. For each housing unit, the selected 11 features include floor space AREA, number of bedrooms BEDROOM, number of halls HALL, number of bathrooms BATHROOM, floor level FLOORLEVEL, house age YEAR, green coverage rate GREENCOVERAGE, decoration DECORATION, distance to CBDDIST_CBD, ring location RING, walking distance to nearest underground railway station UNDERGROUND. Data contain 2160 observations. Sampling spans the period from mid-January to mid-April. Table 1 statistically describes the raw data. We defined various details for house price and the International Conference on Circuits and Systems (CAS 2015) © 2015. The authors - Published by Atlantis Press 349 ===== PAGE 2 (score 61, numeric-paren 1, table-words 22, var-words 11) ===== 11 features as follows. House price is call price listed on websites[34, 35]. The call price is a supply-side price. Floor space is limited to 50 to 200 square meters. Distance to CBD is that to Yan Shi Kou (city center). The city government has planned that Yan Shi Kou is CBD, where business buildings and activities become highly concentrated. Ring location (dummy variable) includes core area within Ring Road No. 1 (=1), ring zone 1 between Ring Road No. 1 and Ring Road No. 2 (=2), ring zone 2 between Ring Road No. 2 and Ring Road No. 3 (=3), and ring zone 3between Ring Road No. 3 and Around-the-city Expressway (=4). There are two underground railway lines currently available in Chengdu. Walking distance to nearest underground railway station is divided into five zones and corresponding dummy variables: up to 400 meters (below five minutes) denoted by 1401 to 800 meters (five to ten minutes) denoted by 2801 to 1200 meters (ten to fifteen minutes) denoted by 3, 1201 to 1600 meters (fifteen to twenty minutes) denoted by 4, and above 1600 meters (above twenty minutes) denoted by 5. TABLE I. STATISTICAL DESCRIPTION FOR THE RAW DATA Mean Median Maximum Minimum Standard. Deviation Jarque-Bera P-value PRICE 97.8 88 365 43 41.1 1354.7 0.00 AREA(sq. meters) 99.7 91.95 223 49 28.1 87.2 0.00 DECORATION 3.1 4 4 1 1.3 30.1 0.00 BEDROOM 2.5 2 5 1 0.7 7.3 0.03 HALL 2.0 2 3 1 0.3 368.5 0.00 BATHROOM 1.5 1 3 1 0.6 20.5 0.00 FLOOR LEVEL 9.8 7 33 1 8.1 46.2 0.00 YEAR 8.6 8 29 1 4.7 68.6 0.00 GREENCOVERAGE(%) 29.3 30 70 1.7 12.5 19.9 0.00 RING 2.6 3 4 1 1.1 13.2 0.00 DIST_CBD(meters) 4939.3 5270 12867.8 201 2661.3 0.4 0.81 UNDERGROUND 4.1 5 5 1 1.3 35.5 0.00   IV. ESTIMATION AND ANALYSIS We took a stepwise regression strategy. Preliminary regression (Table II) suggested that five out of 11 variables were statistically insignificant. However, we found that removing RING and HALL from the regression did not increase the values of R-squared and adjusted R-squared, but led to worsening heteroskedasticity. Thus, we could not remove more features than two from the regressions. We decided the estimated results in Table III. Fitness appears to be satisfactory, figure. 1. TABLE II. RESULTS OF PRELIMINARY REGRESSION. Variable Estimate t-statistic P-value LOG(AREA) 0.89 8.35 0.00 LOG(DECORATION) 0.04 1.59 0.11 LOG(BEDROOM) 0.07 0.89 0.37 LOG(HALL) 0.07 0.80 0.42 LOG(BATHROOM) 0.06 1.10 0.27 LOG(FLOORLEVEL) -0.01 -0.84 0.40 LOG(YEAR) -0.16 -5.22 0.00 LOG(GREENCOVERAGE) 0.05 2.79 0.01 LOG(RING) 0.01 0.12 0.91 LOG(DIST_CBD) -0.09 -2.04 0.04 LOG(UNDERGROUND) -0.08 -1.90 0.06 Error term 1.31 2.30 0.02 Notes: R-squared=0.72, Adjusted R-squared=0.70, S.E. of regression=0.19, F-statistic=39.2. White Heteroskedasticity(P-value)=35.7(0.03). Figure 1. Actual and fitted values for house prices Estimates are significant for AREA, DECORATION, YEAR, GREENCOVERAGE, DIST_CBD, and UNDERGROUND. Hence, given other features constant, the growth of 1% in distance from the CBD implies that the price reduces by about 0.1%. However, a 1% growth in floor area implies that the house price grows by 0.92%. A growth of 10% in green coverage rate may provoke a rise of 0.5% in price. Given other features constant, for an average one-year ‘new’ unit, the implicit price is 1(-0.17)=1 yuan RMB/sqm. For an average 5-year unit, the implicit price is 5(-0.17)=0.76 -.8 -.4 .0 .4 .8 3.5 4.0 4.5 5.0 5.5 6.0 25 50 75 100 125 150 175 Residual Actual Fitted 350 ===== PAGE 3 (score 47, numeric-paren 1, table-words 14, var-words 15) ===== yuan RMB/sqm. For an average 10-year unit, the implicit price is 10(-0.17)=0.68 yuan RMB/ sqm, and so on. Hence, house prices normally decrease marginally with house ages, figure. 2. TABLE III. RESULTS OF STEPWISE REGRESSIONS Variable Estimate t-statistic P-value LOG(AREA) 0.92 9.06 0.00 LOG(DECORATION) 0.05 1.68 0.09 LOG(BEDROOM) 0.07 0.94 0.35 LOG(BATHROOM) 0.06 1.06 0.29 LOG(FLOORLEVEL) -0.01 -0.84 0.40 LOG(YEAR) -0.17 -5.38 0.00 LOG(GREENCOVERAGE) 0.05 2.76 0.01 LOG(DIST_CBD) -0.09 -3.83 0.00 LOG(UNDERGROUND) -0.07 -1.85 0.07 Error term 1.19 2.60 0.01 Notes: R-squared=0.72, Adjusted R-squared=0.70, S.E. of regression=0.19, F-statistic=48.2, White Heteroskedasticity(P-value)=34.0(0.01). Figure 2. House prices decreased marginally with age Given other features constant, for an average unit, the implicit price for the walking distance of 400 to 800 meters to nearest ground station, relative to the walking distance of within 400 meters, reduces by 4.7% (2(-0.07)-1). The implicit price for the walking distance of 801 to 1200 meters to station reduces by 7.4%. The implicit price for the walking distance of 1201 to 1600 meters to station reduces by 9.2%. The implicit price for the walking distance exceeding 1600 meters to station reduces by 10.7%. Thus, effects of walking distance to rail station on house prices are nonlinear and marginal, fig.3. Figure 3. House prices decreased nonlinearly with walking distance to nearest underground stationgiven the price in the zone within 400 meters (=1) V. CONCLUSIONS It has been argued that house prices vary inversely with distance or travel time to the CBD. Various structural and environmental features impact housing prices. Few studies analyzed the implicit prices in Chengdu. The Chengdu metropolis is characterized by a monocentric pattern and has rapidly expanded in terms of economy, population and built area over the past decade. Two underground railway lines are in effect. Five others are in construction. We selected ordinary-type older housing units within the Around-the-city Expressway. The paper employed a hedonic technique and conducted regressions using log-linear equations. We find that house prices decrease with distance from CBD and walking distance to the nearest underground station. The finding supports a negative price gradient from the CBD. In addition, larger floor space, good decoration and higher green coverage increase but age reduces house prices. We suggest that the government could impose higher property tax rates on units with a larger floor space than those with a smaller floor space. ACKNOWLEDGMENTS A Project Supported by Scientific Research Fund of SiChuan Provincial Education Department (2014-2015). REFERENCES [1] Alonso, W., Location and land use. Cambridge, MA.: Harvard University Press, 1964. [2] Fujita, M., Spatial patterns of residential development. Journal of Urban Economics, 12(1), pp. 22-52, 1982. [3] Bender, B. & Hwang H.-S., Hedonic housing price indices and secondary employment centers. Journal of Urban Economics, 17(1), pp. 90-107, 1985. 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