The economic value of streets: mix-scale spatio-functional interaction and housing price patterns

Location factors are vital elements for describing housing price variation. However, limited studies have explicitly illustrated the relationship between urban design and the heterogeneity of housing price patterns. This article specifically evaluates how the interactions between the spatial layouts and land-use system at various scales through street network affect the valuation of the residential properties and the segmentation of housing markets in a network-based Mixed-scale Hedonic Model (MHM) where the submarkets pattern are determined and annotated by the spatially varying estimates on streets. The application of the delivered method in the case of Shanghai City, China, confirms the necessity of using the non-Euclidean distance metric and represent the coexistence between the stationarity and the non-stationarity of the introduced street accessibility variables. The results provide evidence that the impacts of street accessibility measures on the local levels showcase significant spatial variation. It is common for all the places that the properties located on the streets with the higher levels of angular closeness, smaller values of angular betweenness and longer angular distance to the nearby land-uses at the larger scales will be bided higher. It is proven that our delineation of submarket performs better in prediction accuracy than the traditional submarket specifications. The detected submarkets pattern yields that reachable land-use diversity at the pedestrian level is not a preferred factor in the housing submarkets located in the developed city centres. The signs of the price effects of the angular distance to local land-uses distinguish the developing submarkets as two main groups with different degrees of geometrical walkability. It is suggestive that continuously developing pedestrian-oriented neighbours in the walkable areas could contribute to decelerating the growth of house price in Chinese cities. The productions of this study can enrich the understanding of the socioeconomic effects of urban design with greater spatial precision across submarkets.     

轨道交通对沿线住宅价格影响的分市场研究——以深圳市龙岗线为例

研究轨道交通对沿线住宅价格的影响有助于将轨道交通带来的外部效益转化为内部收益,对城市地铁的建设和运营具有重要意义。在真实城市空间中,由于空间异质性的存在,轨道交通对不同区域的影响并非都是相同的,因而需要划定分市场进行研究。本研究以深圳龙岗线郊区段为对象,首先运用Moran'I 指数证实了空间自相关效应的存在,并构造变量将空间自相关效应纳入模型进行控制,结合其他变量构建了4 个Hedonic 模型,然后通过Chow Statistics 检验划定远郊段和近郊段2 个分市场,最后运用最优的Hedonic 模型对轨道交通影响住宅价格的分市场进行研究并解释其形成原因。研究结果表明：① 轨道交通在近郊段对沿线住宅价格具有正向影响;② 轨道交通对沿线住宅价格影响存在分市场效应,不同分市场中轨道交通对沿线住宅价格的影响程度差异较大,龙岗线近郊段对沿线住宅价格具有正向影响,远郊段对沿线住宅价格具有负向影响;③ 可以通过Chow Statistics检验来划定分市场,龙岗线近郊段和远郊段的分界点为“六约—丹竹头”,近郊段分市场的空间范围可能与城市的平均通勤时间有关;④分市场的形成原因是空间异质性,龙岗线郊区段的分市场是由两个区域中心的空间分布差异所导致。     

厦门市住房市场的空间分割及其成因解析

以住房子市场理论为框架建立研究假设,用主成份分析和聚类分析法刻画厦门市的住房市场特征。对住房类型子市场与聚类划分子市场进行对比分析,并采用Hedonic多重回归法对不同子市场划分方式的合理性进行验证。在此基础上,采用特征价格函数和子市场样本密度分布展开空间分析。结果显示:厦门市区划分为四个住房子市场,由城市中心地区向外呈现非均衡的层级化空间分布特征。城市中心与外围局部地区子市场的极化分布形成了"空间分割",降低了住房消费的空间替代性。多元产权结构下的市场环境、非均衡的住房用地拓展以及缺乏弹性的住房开发控制共同导致了空间分割。充分认识住房市场的结构与特征,将有助于提高城市住房公共政策的效率。     

Housing-market bubble adjustment in coastal communities - A spatial and temporal analysis of housing prices in Midwest Pinellas County,Florida

This study aims to investigate the spatial and temporal dynamics of housing prices associated with the 2007 U.S. housing-market bubble in Midwest Pinellas County, Florida. Two methods were used to examine the spatial and temporal dynamic of price levels: housing characteristic influence estimation and a hedonic modeling approach. Two consistent spatial patterns emerged in the estimated coefficients associated with various housing characteristics, with definitive changes occurring at 600 m and 2200 m from the coast. These changes suggested the existence of three geographical submarkets: a coastal, an intermediate or transitional, and an inland submarket. Three temporal stages were observed from the coefficient trends associated with various housing characteristics: 2002–2005 (bubble-growing), 2006–2008 (bubble-burst), and 2009–2011 (post-bubble). The hedonic models demonstrated a complexity in the determinant of housing prices during the housing peak-bubble period. The inclusion of submarket dummy variables in the hedonic models improved the amount of explained variation. The R² values of the hedonic models from 2002 through 2008, followed by a decrease after the bubble-burst period. The pre-bubble-burst trend in R² suggests that predictable market forces were at work; partly driven by irrational expectations of housing buyers and the perceptions of run-away housing prices during the growth phase of the bubble.     

区域旅游目的地竞争优势的空间聚类分析——以浙江省为例

基于两维图论聚类方法,将主成分分析与两维图论聚类分析有机结合,建立区域旅游目的地竞争优势综合评判模型.首先,建立区域旅游业竞争优势的评价指标体系;接着,利用主成分分析方法对区域旅游竞争优势进行综合评估,并将其应用于浙江省旅游竞争优势评价;最后,以各个旅游目的地的主成分因子为分类对象,采用两维图论聚类法,对各地的旅游竞争优势进行空间聚类分析.研究结果表明,主成分分析与两维图论聚类分析有机结合研究区域旅游目的地的竞争优势是可行的,两维图论聚类能较好地反映区域旅游竞争优势和空间相关性,其结果将有助于决策者制定区域旅游发展战略.     

广州市社会空间的因子生态再分析

运用因子分析、聚类分析方法对广州市中心区1990年人口普查数据进行了研究.从9类47个变量中抽取出5个形成广州市社会区类型的主因子,根据各主因子的得分,把广州市中心区划分为七类社会区,并把形成广州市社会空间分异现象的原因归结为五类.通过与1985年类似研究结果的比较,发现广州市社会空间结构的分异现象更趋明显,具体反映在人口“外溢”、居住条件改善及新开发区的形成等方面.最后就如何引导广州市社会空间结构演变进行了讨论.     

鄱阳湖湖区核心生态系统服务功能空间辨识

根据千年生态系统评估提出的评估概念框架,考察鄱阳湖湖区生态系统服务功能的形成机理,构建了鄱阳湖湖区生态系统服务功能表征指标体系,并利用多主题1km栅格成分数据模型,采用主成分分析法提取了鄱阳湖湖区核心生态系统服务功能综合表征指标。在此基础上,通过空间聚类分析,形成了鄱阳湖湖区生态系统服务功能分区,实现了各分区单元上生态系统服务功能的空间辨识。研究结果表明,鄱阳湖湖区核心生态系统服务功能包括了支持功能、供给功能、调节功能和文化功能,它们在空间上的排列组合形成了鄱阳湖湖区的10个生态系统服务功能分区,本文辨识了鄱阳湖湖区精细栅格水平上核心生态系统服务功能之间的差异及其空间分异特征。本研究提出的生态系统服务功能分区及其空间辨识方案,为鄱阳湖湖区生态系统服务功能的开发与保育提供决策参考信息。     

The modifiable areal unit problem in hedonic house-price models

When geographically aggregated data are included in hedonic models, the resulting coefficients are biased by the spatial scale and spatial configuration of variable measurement. We explore the effects of this modifiable areal unit problem (MAUP) within the context of hedonic price models with an individual-level dependent variable. Specifically, we developed standard and spatial hedonic regression models in order to examine the effects of the MAUP on model fit and coefficient estimates. Our empirical analysis documents several significant scale and zoning effects in the hedonic modeling framework. First, neighborhood characteristics are clearly important in efforts to improve model fit—and they are more significant contributors in the standard model than in the spatial hedonic model. For aggregation scale, the model fit change of the standard model is relatively large, whereas the change is more modest for spatial models. The patterns of change in model fit for standard and spatial hedonic models clearly diverge from one another, implying the existence of a scale level showing a maximum functional range of the submarket on which scale dependencies are expected to have an impact. Regarding the zoning effect, the model fits for both standard and spatial hedonic models vary according to the submarket systems.     

Data-driven approach to characterize urban vitality: how spatiotemporal context dynamically defines Seoul’s nighttime

ABSTRACT

This study takes a data-driven approach to define urban nighttime by examining the spatiotemporal dynamics of urban vitality. Using micro-scale spatiotemporal analysis, this paper empirically provides a comprehensive, yet granular, picture of collective human behaviors in cities. Using Seoul, South Korea as a case study site, it prioritizes the spatiotemporal context in order to mitigate uncertain contextual effects inherent in such forms of data-driven analysis. Instead of leaving the data re-grouping up to researcher’s arbitrary decision, this paper employs a functional principal component analysis (FPCA) to systematically transform a set of discrete data to a continuous functional form. This paper applies FPCA on 24-hour-based dataset of pedestrian traffic in Seoul in order to make a data-driven extraction of principal components that characterize the city’s unique patterns of urban vitality. Extracting principal components allows for less statistically obvious phenomena to be measured that would have otherwise been hidden within the data. This approach proved successful in capturing nighttime vitality patterns that are eclipsed by the overwhelming trend of daytime patterns. Additionally, this paper compares differences between regions and seasons to examine what the differences can tell about the definition of nighttime.     

城市轨道交通对沿线住宅价格的时空效应——以福州地铁1号线为例

随着中国城市轨道交通的大力发展,城市轨道交通对沿线住宅价格的影响引起广泛关注,探索其时空效应有助于预测未来新建轨道交通的影响以及帮助政府制定合理的房价调控和城市土地利用政策。以福州地铁1号线为例,探讨城市轨道交通从建设前到建成运营的过程中,对站点周边2 km范围内住宅价格产生的时空效应。结果表明：① 城市轨道交通沿线住宅价格受到区位、邻里、建筑等多方面因素的综合作用,其中,城市轨道交通对沿线住宅价格有显著的增值效应,中高价位住宅市场受到城市轨道交通的影响最为明显。② 空间维度上,住宅价格随着与轨道交通距离的增大而呈现不同程度的递减,至轨道交通站点距离每减少1 km,住宅价格增加5.1 %。轨道交通对沿线住宅价格具有显著的分市场效应,中心城区市场的影响半径大于非中心城区市场,平均空间影响范围为1.5 km;而非中心城区市场的影响强度要远高于中心城区市场,在400 m范围内住宅价格受到轨道交通的影响最大。③ 时间维度上,城市轨道交通在建设期和运营期对沿线住宅价格均呈现出正向影响,且运营期对住宅价格的影响显著高于建设期,运营期相较于建设期平均涨幅达19.63 %;同时,轨道交通的开通给住宅价格带来的效应远大于调控政策以及宏观经济因素的影响。     

基于空间扩展模型和地理加权回归模型的城市住房价格空间分异比较

鉴于已有研究主要集中探讨住房价格的空间依赖性,较少涉及空间异质性对住房特征价格的影响,也很少尝试构建不同计量模型来比较模型间刻画住房价格影响因素空间分异的准确性,以长沙市中心城区为研究区,采用空间扩展模型和地理加权回归模型比较分析城市住房价格影响因素的空间分异,结果表明：① 空间扩展模型和地理加权回归模型都表明,长沙市中心城区的住房属性边际价格随着区位的变化而变化,揭示住房价格影响因素具有显著的空间异质性;小区环境、交通条件、教育配套、生活设施等因素对住房价格的影响强度存在明显的空间分异。② 地理加权回归模型和空间扩展模型都能对传统特征价格模型进行改进,但地理加权回归模型在解释能力和精度方面都超过空间扩展模型;对属性系数估计空间模式的分析,地理加权回归模型形成的结果比采用坐标多义扩展的空间扩展模型更为复杂和直观。     

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.     

Segmentation of the Housing Market and its Determinants: Seoul and its neighbouring new towns in Korea

This study identifies the spatial pattern of housing price changes and their determinants in Seoul and its neighbouring new towns. The results of a cluster analysis show that the spatial pattern of housing price change rates is nor correlated with housing prices. It is found that housing price change rates vary with the Chonse price index and money supply, building permit area, stock price index, and yield on 3 year corporate bonds. It is also shown that the more apartment units that are built in a region, the lower the housing price change rate.     

A Coregionalized Model to Predict Housing Prices

Abstract

In a uniequational hedonic model, the main source of spatial dependence is found in the explained variable, since the price of a house mainly depends on the housing prices in the neighborhood (although this can also be due to other factors, such as missing covariates and the model of choice). Dependence is one of the primary causes of spatial autocorrelation in disturbances. However, such disturbances may also be spatially correlated with the disturbances of other equations; in this case, they can be considered coregionalized. This paper presents a multi-equational hedonic regression model with coregionalized disturbances and heterotopic data. The model comprises two equations. The first explains housing prices using data from a sample, while the second explains an auxiliary variable, quality of the area, obtained from a different sample. The model is then applied practically to predict housing prices. [Key words: Cokriging, housing prices, geostatistics, multi-equational hedonic model, coregionalized.]     

Analysis of spatial patterns of public attention on housing prices in Chinese cities: A web search engine approach

Housing price has become one of the most pressing issues facing urban residents in China in recent years and received considerable attention. However, detailed housing price data are often ill-documented or unavailable for the public, thus posing a grand challenge for the study of housing prices in China. Because individuals' Internet search activities can be recorded by web search engines, the analysis of these web search activities in cyber-space may provide a means of better understanding public attention and associated concerns in real geographic space. In this study, we focus on exploring the spatial patterns of public attention on housing price through the analysis of web query activities based on Baidu Index, a Chinese keyword analysis tool from Baidu web search engine. We propose a new index based on keyword query outcome from Baidu search database to analyze spatially heterogeneous patterns of housing price attention from 19 large and medium-sized cities in China. We evaluate the spatial network structure of housing price attention, and develop a new index to measure the intensity of interaction relationships among cities of interest. Our results show that spatial interactions of housing price attention between cities evaluated using the new method are consistent with those from a gravity model. Meanwhile, as revealed from Baidu Index-based indicators, strong spatial association patterns exist among cities that form urban agglomerations. Further, our results demonstrate that the web search engine approach, based on the coupling of cyber-space and geographic space, provides solid support for the study of housing price attention and its spatially explicit patterns in China.     

扬州市住宅价格的空间分异与模式演变

以2001-2012 年扬州中心城区各居住小区的住宅平均单价为基本数据,通过建立住宅价格总体分异测度指数(GDI) 计算其总体分异趋势及各住宅类型内部的分异趋势;采用核密度函数等方法探索住宅价格的分布形态和分异格局的演变规律;利用趋势面分析不同住宅类型价格的空间分异趋势;基于上述结果总结空间分异的演变模式,并分别探索空间分异与格局演变的驱动力。结果表明：① 2001 年以来扬州市住宅价格差距显著增大,分异趋势在波动中增强,与城市住宅均价的年增长率耦合;住宅价格呈现西高东低的空间分异格局,同档次价格小区由空间集聚转为相对分散,高、低价格住宅区分别沿固定扇面由中心向外围扩散。②不同住宅类型内的价格分异走势差别显著,各类型住宅间的价格趋势面差距明显,但其空间形态类似。③ 空间分异模式由2001 年西高东低的扇形同档次价格集聚式分异转变为2012 年扇形与圈层相结合的多档次价格混合式分异。④ 2001 年以来住宅价格总体分异的核心驱动力是城市居住空间的迅速扩展、居民收入差距的增大、房地产市场的繁荣和住宅类型的多元化,其住宅价格空间格局演变的驱动力为城市发展方向的确立与变化、特定住宅类型建设的区位指向和古城保护、旧城改造与新区建设。     

BUILDING UPON THE FOUNDATIONS OF GENTRIFICATION: INNER-CITY HOUSING DEVELOPMENT IN AUSTRALIA IN THE 1990s

In three recent papers, Bourne has suggested that the rate of gentrification in Canadian cities will decline, for both demand and supply reasons, notwithstanding economic recovery in the mid- 1990s. This prognosis for Canadian cities is so much at odds with the activity currently being generated in the upper-income housing markets at the center of the five largest Australian cities that it invites further investigation. Evidence is presented of recovery in the inner-suburban terrace housing submarket, and what began as countercyclical investment in the Australian equivalent of the loft-conversion and condominium submarkets. This latest phase of residential revitalization in the inner city heralds a trend to higher-value, high-rise living at the center of Australian cities. By the early 1990s enough Australians had become conditioned to inner-area living to provide optimism about the underlying demand, while the projected yields brought condominium development into the realm of financial feasibility. But concerted government action also was necessary to prime the core-area market for residential project development at the time (1991–1993). This raises a series of interesting questions about the advisability of state governments pursuing a property-led development strategy in recovery when the Anglo-American experience suggests that it might be short-lived at best. Lastly, some consideration is given to the implications for gentrification research and theory of possible divergence through the 1990s in the experience of North American and Australian cities.     

The impact of spatial scale: exploring urban butterfly abundance and richness patterns using multi-criteria decision analysis and principal component analysis

ABSTRACT

Modelling changes in biodiversity have become a necessary component of smart urban planning practices. However, concepts such as biodiversity are often evaluated using area-based composite indices, the results of which are heavily reliant on specific parameters chosen. This paper explores the design and implementation of a butterfly biodiversity index by comparing two widely accepted modelling techniques: principal component analysis and spatial multi-criteria decision analysis (MCDA). A high degree of scale dependency has been demonstrated in previous studies exploring the use of area-based composite measures. To evaluate the impact of scale, each model was assessed at two different spatial resolutions. The outcomes were analyzed, mapped and compared using ordinary least squares, geographically weighted regression and global Moran’s I to evaluate relative biodiversity patterns across the City of Toronto, Canada. Findings indicate that the impact of spatial scale was significant, whereby the coarser resolution models were found to be more highly correlated with biodiversity, compared to the finer resolution models. The results of this study contribute to a growing body of literature that explores key conceptual questions regarding the robustness of GIS-based MCDA, the impact of scale in urban ecology studies, and the use of composite indices to manage spatial ecological data.     

基于主成分分析法的湖南省农用地集约利用评价

采用主成分分析方法,从市(州)层面对湖南省农用地集约利用进行定量综合评价,并依据评价结果进行聚类分析。结果表明:2008年,湖南省农用地可划分为集约利用、中度集约利用、低度集约利用三等,归并为6类;环洞庭湖地区及湘江流域的岳阳、常德、益阳、长沙、湘潭、衡阳、永州7市农用地处于集约利用状态,其中,长沙、湘潭归并为一类,岳阳、益阳、衡阳归并为一类,常德、永州归并为一类;湘中娄邵盆地的娄底、邵阳和湘东的株洲3市农用地处于中度集约利用状态,归并为一类;湘西的怀化、湘西州、张家界和湘南的郴州4市(州)农用地处于低度集约利用状态,其中,郴州、怀化归并为一类,张家界、湘西州归并为一类。研究结果符合区域农用地利用实际,对湖南省农用地集约高效利用具有一定的指导意义。     

An ex-ante analysis of housing location choices due to housing displacement: The case of Bristol Place

Urban renewal has been criticized for its unintended effects on the displacement of residents to housing situations where they are worse off. The literature concerned with housing displacement focuses on after the fact (ex post) analysis of mobility, hence providing only partial insights to prospective outcomes for residents. In this article, we develop a methodology for predicting before the fact (ex ante) spatial location outcomes for residents facing forced displacement. We ask where in the city will displaced residents locate and how do potential new locations compare to the current one? We apply this methodology to an ongoing urban renewal project in Illinois in order to predict spatial relocation patterns. Our findings show that residents facing forced displacement are likely to relocate to low-priced neighborhoods that are on average as bad or worse off in terms of housing adequacy, accessibility to labor, and shopping. These findings not only bear important implications for urban redevelopment policy, but also help to reveal spatially determined costs of moving which are not factored into current redevelopment impact evaluations.