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1.
兰州市商品住宅价格的空间分异规律   总被引：1，自引：0，他引：1  
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。  相似文献   

2.
城市房价空间分布及其影响因素分析   总被引：1，自引：0，他引：1  
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。  相似文献   

3.
Area-to-point Kriging in spatial hedonic pricing models   总被引：4，自引：1，他引：3  
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.  相似文献   

4.
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.
Christopher BitterEmail:
  相似文献   

5.
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.  相似文献   

6.
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)不同因子对价格影响程度不同,其中对南昌市二手房价格影响较大的因子是房龄、绿化率以及与CBD的距离。3)同一因子对住房价格的影响在不同空间也具有差异性。其中主要是绿化率、容积率、重点学校、购物中心及地铁对新开发区的二手房价格影响比较大,对老城区影响较小;商务中心区和三甲医院对南昌县二手房价的影响最大;而房龄和旅游景点对老城区影响比较大。  相似文献   

7.
Simulating the dynamics and processes within a spatially influenced retail market, such as the retail gasoline market, is a highly challenging research area. Current approaches are limited through their inability to model the impact of supplier or consumer behavior over both time and space. Agent‐based models (ABMs) provide an alternative approach that overcomes these problems. We demonstrate how knowledge of retail pricing is extended by using a ‘hybrid’ model approach: an agent model for retailers and a spatial interaction model for consumers. This allows the issue of spatial competition between individual retailers to be examined in a way only accessible to agent‐based models, allowing each model retailer autonomous control over optimizing their price. The hybrid model is shown to be successful at recreating spatial pricing dynamics at a national scale, simulating the effects of a rise in crude oil prices as well as accurately predicting which retailers were most susceptible to closure over a 10‐year period.  相似文献   

8.
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.  相似文献   

9.
This paper examines the use of multi‐agent simulations (MAS) to model the petrol market. The development of a purely agent based model for petrol prices is presented. Failings within this model are discussed and an alternative strategy for controlling the price of each petrol station based on population of customers is considered. Individual level modelling of customers is too computationally intensive; therefore a spatial interaction model was used to estimate the sales and linked to the agent system to create a hybrid model. To evaluate how effective this hybrid model was, a comparison was made with an existing data set of real petrol prices collected over a two month period. This was achieved both statistically and visually with the use of a Geographical Information System (GIS). Experimentation revealed that the hybrid model outperformed the agent model. Investigation into the behaviour and sensitivity of the system (for example, how prices diffuse spatially) was undertaken by means of idealised simulations.  相似文献   

10.
We “spatialize” residual-based panel cointegration tests for nonstationary spatial panel data in terms of a spatial error correction model (SpECM). Local panel cointegration arises when the data are cointegrated within spatial units but not between them. Spatial panel cointegration arises when the data are cointegrated through spatial lags between spatial units but not within them. Global panel cointegration arises when the data are cointegrated both within and between spatial units. Spatial error correction arises when error correction occurs within and between spatial units. We use nonstationary spatial panel data on the housing market in Israel to illustrate the methodology. We show that regional house prices in Israel are globally cointegrated in the long run and there is evidence of spatial error correction in the short run.  相似文献   

11.
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.  相似文献   

12.
The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.  相似文献   

13.
The neighborhood effects of foreclosure   总被引：1，自引：0，他引：1  
Neighborhood quality is an important attribute of housing yet its value is rarely known to researchers. We argue that changes in nearby foreclosures reveal changes in neighborhood quality. Thus estimates of the hedonic price of nearby foreclosures provide a glimpse of values that people hold for local neighborhood quality. The empirical models include controls for both spatial dependence in housing prices and in the errors. The estimates indicate that nearby foreclosures produce externalities that are capitalized into home prices—an additional foreclosure within 250 feet of a sale negatively impacts selling price by approximately $1,666, ceteris paribus.  相似文献   

14.
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.  相似文献   

15.
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.   相似文献   

16.
西安市住宅价格空间结构和分异规律分析   总被引：1，自引：0，他引：1  
宋雪娟  卫海燕  王莉 《测绘科学》2011,36(2):171-174
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。  相似文献   

17.
This paper presents a GIS-based decision support system prototype intended for use by public housing authority (PHA) administrators and planners designing policy for housing mobility programs. Housing mobility programs enable low-income families, many of whom live in government-operated public housing, to move to more desirable private-market rentals via rent subsidies. Unfortunately, housing authority planners have limited ability to visualize alternative relocation schemes of cohorts of low-income families or the impacts associated with these relocation policies. Thus, they are often not able to give highest-quality advice to clients regarding places to search for private-market rental housing. Housing Location Planner assists PHA planners in three ways: it analyzes spatial, demographic and housing market characteristics of the study area, selects certain portions of the study area for input to an optimization model which generates alternative family allocations, and displays optimization model results in a way that links decision variable values and objective function values. Housing Location Planner is seen as a first step in the development of even more sophisticated multi-stakeholder spatial decision support systems for subsidized housing planning in which one or more alternative allocations of families across a study area is chosen as a basis for policy initiatives. Received: 8 September 1999/Accepted: 23 October 2000  相似文献   

18.
The Ruhr is an “old acquaintance” in the discourse of urban decline in old industrialized cities. The agglomeration has to struggle with archetypical problems of former monofunctional manufacturing cities. Surprisingly, the image of a shrinking city has to be refuted if you shift the focus from socioeconomic wealth to its morphological extension. Thus, it is the objective of this study to meet the challenge of modeling urban sprawl and demographic decline by combining two artificial intelligent solutions: The popular urban cellular automaton SLEUTH simulates urban growth using four simple but effective growth rules. In order to improve its performance, SLEUTH has been modified among others by combining it with a robust probability map based on support vector machines. Additionally, a complex multi-agent system is developed to simulate residential mobility in a shrinking city agglomeration: residential mobility and the housing market of shrinking city systems focuses on the dynamic of interregional housing markets implying the development of potential dwelling areas. The multi-agent system comprises the simulation of population patterns, housing prices, and housing demand in shrinking city agglomerations. Both models are calibrated and validated regarding their localization and quantification performance. Subsequently, the urban landscape configuration and composition of the Ruhr 2025 are simulated. A simple spatial join is used to combine the results serving as valuable inputs for future regional planning in the context of multifarious demographic change and preceding urban growth.  相似文献   

19.
房地产计税价格批量评估实证研究   总被引：3，自引：0，他引：3  
结合批量评估的技术思路,以深圳市二手住宅计税价格批量评估为例,构建了以长期趋势法,成本法为基本原理,结合地理信息技术(GIS)的批量评估模型,并提出研究结论与未来发展设想.  相似文献   

20.
The location of new homes defines the urban–rural fringe and determines many facets of the urban–rural interaction set in motion by construction of new homes in previously rural areas. Home, neighborhood and school district characteristics play a crucial role in determining the spatial location of new residential construction, which in turn defines the boundary and spatial extent of the urban–rural fringe. We develop and apply a spatial hedonic variant of the Blinder (J Hum Resour 8:436–455, 1973) and Oaxaca (Int Econ Rev 9:693–709, 1973) price decomposition to newer versus older home sales in the Columbus, Ohio metropolitan area during the year 2000. The preferences of buyers of newer homes are compared to those who purchased the nearest neighboring older home located in the same census block group, during the same year. Use of the nearest older home purchased in the same location represents a methodology to control for various neighborhood, social–economic-demographic and school district characteristics that influence home prices. Since newer homes reflect current preferences for home characteristics while older homes reflect past preferences for these characteristics, we use the price differentials between newer and older home sales in the Blinder–Oaxaca decomposition to assess the relative significance of various house characteristics to home buyers.
Joni S. CharlesEmail:
  相似文献   

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