Measuring the impact of pollution on property prices in Madrid: objective versus subjective pollution indicators in spatial models

Much work has been done in the context of the hedonic price theory to estimate the impact of air quality on housing prices. Research has employed objective measures of air quality, but only slightly confirms the hedonic theory in the best of cases: the implicit price function relating housing prices to air pollution will, ceteris paribus, be negatively sloped. This paper compares the performance of a spatial Durbin model when using both objective and subjective measures of pollution. On the one hand, we design an Air Pollution Indicator based on measured pollution as the objective measure of pollution. On the other hand, the subjective measure of pollution employed to characterize neighborhoods is the percentage of residents who declare that the neighborhood has serious pollution problems, the percentage being referred to as residents’ perception of pollution. For comparison purposes, the empirical part of this research focuses on Madrid (Spain). The study employs a proprietary database containing information about the price and 27 characteristics of 11,796 owner-occupied single family homes. As far as the authors are aware, it is the largest database ever used to analyze the Madrid housing market. The results of the study clearly favor the use of subjective air quality measures.     

Forecasting prices of single family homes using GIS-defined neighborhoods

We estimate spatiotemporal models of average neighborhood single family home prices to use in predicting individual property prices. Average home-price variations are explained in terms of changes in average neighborhood house attributes, spatial attributes, and temporal economic variables. Models adopting three different definitions of neighborhoods are estimated with quarterly cross-sectional data over the period 2000–2004 from four cities in Southern California. Heteroscedasticity and autocorrelation problems are detected and adjusted for via a sequential routine. Results of these models suggest that forecasts obtained using city neighborhood average price equations may have advantage over forecasts obtained using city aggregated price equations.      

Relationship between value of open space and distance from housing locations within a community

This research uses a sequence of hedonic spatial regressions for a metropolitan housing market in the Southeastern United States to explore a new procedure that establishes the relationship between the value attributable to open space and distance from housing locations (a “distance-decay function”) within a given community. A distance-decay function allows identification of the range of distance over which open space affects housing values and the estimation of a proxy for the value added to nearby houses resulting from hypothetical open space preservation. Ex post analyses of the open-space regression coefficients suggest marginal implicit price functions for three types of open space that decay as open space area increases with respect to house location. After controlling for other factors in the spatial hedonic model, simple distance-decay functional relationships were established between the implicit prices of developed open space, forest-land open space, and agriculture-wetland open space and the buffer radius of the open-space areas surrounding a given housing location. The proposed method may be useful for identifying the range over which preferences for different types of open space are exhibited.     

Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data

The accurate mapping of urban housing prices at a fine scale is essential to policymaking and urban studies, such as adjusting economic factors and determining reasonable levels of residential subsidies. Previous studies focus mainly on housing price analysis at a macro scale, without fine‐scale study due to a lack of available data and effective models. By integrating a convolutional neural network for united mining (UMCNN) and random forest (RF), this study proposes an effective deep‐learning‐based framework for fusing multi‐source geospatial data, including high spatial resolution (HSR) remotely sensed imagery and several types of social media data, and maps urban housing prices at a very fine scale. With the collected housing price data from China's biggest online real estate market, we produced the spatial distribution of housing prices at a spatial resolution of 5 m in Shenzhen, China. By comparing with eight other multi‐source data mining techniques, the UMCNN obtained the highest housing price simulation accuracy (Pearson R = 0.922, OA = 85.82%). The results also demonstrated a complex spatial heterogeneity inside Shenzhen's housing price distribution. In future studies, we will work continuously on housing price policymaking and residential issues by including additional sources of spatial data.     

西安市商品住宅价格空间格局的演化研究

针对目前城市住宅价格空间格局演化研究不足,尤其是内在驱动机制研究较少的现状,利用2000年、2004年、2008年及2013年4年节点数据,采用空间自相关指数并结合空间变异函数,分析西安市商品住宅价格空间格局演化特征及其驱动机制,为城市住房政策的制定提供参考。结果表明:住宅价格呈现出显著的空间自相关,热点和冷点区发生转移;住宅价格的空间变异程度不断加大,空间分异格局中的随机成分不断降低,结构化分异越来越显著;住宅价格高值区呈现出由双中心向多中心、多圈层演化的趋势;从城市规划引领、居住空间扩张和交通条件改善3个方面探讨住宅价格空间格局演化的驱动机制。     

南昌市二手房价格影响因子的空间异质性

以安居客网站爬取的2018年10月894个南昌市住宅小区二手房价格为研究对象,利用地理加权回归模型探讨了建筑特征、邻里特征、区位特征等方面各影响因子对住宅价格的作用差异.研究结果表明:1)地理加权回归(GWR)模型的拟合结果优于OLS模型,将回归系数结果空间可视化发现南昌市二手房价格影响因子具有空间异质性.2)不同因子...     

基于区域特征的城市住宅价格评估模型

针对传统住宅价格模型不足,根据地学区位理论,将区域经济因素引入特征价格模型,提出了基于区域特征的城市住宅价格评估模型.依托郑州市数字房产数据库,选取2007～2010年新建商品房买卖合同数据,利展GIS技术获取样本的位置、距离信息,采用多元线性回归方法对该模型进行了验证.结果表明住宅价格与区域经济、位置特征、邻里特征、...     

Exploring spatiotemporal dynamics in a housing market using the spatial vector autoregressive Lasso: A case study of Seoul,Korea

House prices fluctuate spatiotemporally and when influential changes from a region happen, the effects spread out in space over time. Although many studies have introduced various models to explain the spatiotemporal dynamics in housing markets, it is always challenging to consider both dimensions in a model. Some recent studies have identified spatiotemporal interactions of house prices by combining spatial and temporal models via spatial vector autoregression. The approach, however, assumes spatial homogeneity of the variables due to insufficient degrees of freedom. Since the housing market is generally conceived as heterogeneous, we suggest an alternative model of the spatial vector autoregressive Lasso without the homogeneity assumption. As an empirical example, we examine the spatiotemporal interaction between house sales price and rent in Seoul, Korea. The results show that rent for apartments in Gangnam‐gu, a socioeconomic core of Seoul, has positive impacts on rent for apartments in surrounding suburbs rather than their sales price. Moreover, the suggested model outperforms the classical method in terms of explanation, prediction, and autocorrelation of residuals. This research is expected to provide a methodological guide to explore the interaction between house sales price and rent, and insights into the spatiotemporal dynamics of the housing market in Seoul.     

兰州市商品住宅价格的空间分异规律

针对住宅价格在城市空间中的分布规律问题,该文以兰州市主城区2015年在售的187个商品住宅样本均价为基本数据,运用空间自相关法对兰州市住宅价格的空间异质性和集聚性进行分析,并利用趋势面分析和空间反距离权重插值法对住宅价格的空间分布格局进行研究。结果表明:兰州市住宅价格总体上呈显著的空间正自相关性,少数地区存在差异性;住宅价格发展不平衡,价格"东高西低";住宅价格由各区行政中心向四周逐级递减,呈多极核分布特征;价格等值线"东密西疏",住宅价格变化幅度空间差异较大。分析发现,区位条件、交通条件及居住环境是影响兰州市商品住宅价格的主要因素。     

Area-to-point Kriging in spatial hedonic pricing models

This paper proposes a geostatistical hedonic price model in which the effects of location on house values are explicitly modeled. The proposed geostatistical approach, namely area-to-point Kriging with External Drift (A2PKED), can take into account spatial dependence and spatial heteroskedasticity, if they exist. Furthermore, this approach has significant implications in situations where exhaustive area-averaged housing price data are available in addition to a subset of individual housing price data. In the case study, we demonstrate that A2PKED substantially improves the quality of predictions using apartment sale transaction records that occurred in Seoul, South Korea, during 2003. The improvement is illustrated via a comparative analysis, where predicted values obtained from different models, including two traditional regression-based hedonic models and a point-support geostatistical model, are compared to those obtained from the A2PKED model.     

西安市住宅价格空间结构和分异规律分析

利用ESDA方法对西安市城区的291个普通住宅项目均价数据进行研究,通过计算Moran指数和半变异函数分析了其空间自相关性和变异性,并进行了趋势分析。应用Kriging空间插值方法对西安市普通住宅价格空间分布进行了模拟。研究结果表明:西安市房价存在显著的空间自相关性,大部分住宅价格呈空间集聚格局,少部分因存在空间异质性而呈离散分布;房价变异函数表现出各向异性,不同方向有不同结构特征,空间自相关尺度为14.2km;西安市房价空间分异规律明显,房价分布格局受城市功能区划和交通影响较大。     

Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method

Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.

基于细粒度人口数据的深圳公园绿地可达性评价

公园绿地是城市公共基础设施的重要组成部分,公园合理的空间布局有助于城市居民便捷和公平地享用其服务功能。根据深圳市建筑普查数据和细粒度的人口数据提取住房信息,结合公园绿地数据和土地利用情况,针对不同类型公园的服务能力分别采用合适的可达性计算方法,从住房、社区和街道三个尺度评价深圳公园绿地可达性,结果表明:社区公园可达性一般,仅57.62%的社区的居民步行500 m以内能到达最近的社区公园;城市公园可达性总体较好,95.87%的社区市民步行前往最近城市公园所需时间在20 min以内。然而,大鹏半岛无城市公园,且多为自然生态保护区,市民到达城市公园所需时间长。社区公园可达性好的区域呈带状分布于深圳南部各区,城市公园高可达性区域呈多核围绕在各区行政中心。     

城市房价空间分布及其影响因素分析

针对城市房价的空间分布规律及其影响因素的研究,该文提出了以南昌市青山湖区房价为研究对象,基于相关理论,搜集整理了2015年07月到10月南昌市青山湖区155个楼盘的均价,利用市场比较法把房价修正到2015年10月份节点上,估算出了155个楼盘点的价格,以GIS技术为研究平台,运用普通克里格插值方法,得到了青山湖区房价的等值线图,根据等值线图得到其空间分布情况,从可达性视角出发,采用结构方程模型构建了青山湖区房价影响因素分析框架,运用SPSS分析出各自变量和因变量之间的关系,即定量分析出了各影响因素对房价格产生的影响程度。     

A spatio-temporal model of housing prices based on individual sales transactions over time

A spatio-temporal model of housing price trends is developed that focuses on individual housing sales over time. The model allows for both the spatio-temporal lag effects of previous sales in the vicinity of each housing sale, and for general autocorrelation effects over time. A key feature of this model is the recognition of the unequal spacing between individual housing sales over time. Hence the residuals are modeled as a first-order autoregressive process with unequally spaced events. The maximum-likelihood estimation of this model is developed in detail, and tested in terms of simulations based on selected data. In addition, the model is applied to a small data set in the Philadelphia area.     

基于房产网站数据的商品住宅价格空间分布研究——以成都市为例

从安居客房产网站自动获取成都市的商品住宅资料,利用GIS方法分析成都市商品住宅价格的空间分布特征,得出了成都市商品房价格空间分布结果和发展趋势。     

利用邻域相似性的居民地要素增量更新质量评估

目的 针对增量更新过程中只对变化对象进行更新的特点,从更新对象的邻域空间相似性入手,以居民地要素为例,对更新对象和源对象的几何相似性及其邻域内对象之间的空间关系相似性进行了形式化表达和计算,基于评估可信度最大化原则确定了对象评估顺序,并根据邻近对象的不同特点设计了两阶段评估流程。实验表明,该方法将评估区域限定在了更新对象邻域范围内,能够有效地发现尺度变换过程中实体及其空间关系错误,提高了更新系统的可用性。     

POPULATION CENSUS WITH THE AID OF AERIAL PHOTOGRAPHS: AN EXPERIMENT IN THE CITY OF LEEDS

This study shows how aerial photographs can be of value in a population census. The census and the enumeration district maps were used initially to obtain population data and the housing stock was derived from the aerial photographs. From these the population densities were determined of a number of sample enumeration districts containing a single type of house. Another set of enumeration districts was selected and the housing stock again derived from the aerial photographs. By considering the type and quantity of housing stock and the population density of each housing type, the population figures were estimated for each enumeration district. The values of these population estimates were then compared with the values recorded in the census. The overall population estimate had an error of only 2%, but the estimates for some of the individual enumeration districts showed greater errors. These errors are assessed and analysed and some suggestions are made to improve the methodology used in this study.     

重庆市房价与地价影响因素实证研究

房价与地价的影响因素多且因素之间相关关系复杂,本次研究通过收集整理重庆市房价与地价的相关数据,采用政策因素、宏观区位因素、微观区位因素、平均容积率因素、规划因素、路网密度因素来研究房价与地价之间的关系,通过建立因素间的通径图构建房价地价的结构方程模型,详细分析了各因素之间的影响关系.     

Predicting road system speeds using spatial structure variables and network characteristics

Spatial regression is applied to GPS floating car measurements to build a predictive model of road system speed as a function of link type, time period, and spatial structure. The models correct for correlated spatial errors and autocorrelation of speeds. Correlation neighborhoods are based on either Euclidean or network distance. Econometric and statistical methods are used to choose the best model form and statistical neighborhood. Models of different types have different coefficient estimates and fit quality, which might affect inferences. Speed predictions are validated against a holdout sample to illustrate the usefulness of spatial regression in road system speed monitoring.