空间分析在住宅价值评估中应用研究综述

住宅是一种异质性商品，它的构成使用价值的各个特征之间有明显的差异，因此特征价格定价模型广泛用于住宅的价值评估。但是由于住宅数据中常常表现出空间问题，使得传统的特征价格模型不再适用，空间效应在不动产领域的存在引起了学者们的广泛关注。对空间分析在住宅价值评估中的应用研究进行了回顾和阐述，首先分析空间效应的来源和影响，讨论了在传统模型中纳入空间效应的重要性，接着阐述如何对这些空间效应进行有效建模和检验，并分析了进一步研究的方向。     

Do housing prices promote total factor productivity? Evidence from spatial panel data models in explaining the mediating role of population density

The issue of what to promote in total factor productivity (TFP) in urban areas has been widely discussed in academia and housing prices and population density are confirmed to be two of the most essential driving factors. However, research into the interaction of housing prices and population density with TFP has been neglected, with no previous studies taking spatial factors into consideration, which may bias the results. From this perspective, using spatial panel data models and employing instrumental variables to solve the endogenous problem, this study examines the impact of housing prices on TFP through the mediating effect of population density for 283 Chinese cities during the period 2000–2013, and confirms that the mediating effect accounts for 18.70 % of the total effect. The results show the positive and significant association of housing prices with TFP and the inverted U-shape of population density. The underlying logic is that housing prices change population density by attracting people with high purchasing power and discouraging those unable to afford housing, whereas increased density helps to promote productivity since the settled inhabitants always have highly developed work skills and are well educated. The influencing mechanism of housing prices on TFP through population density is analyzed, namely the spillover effect. We find that the spillover effect exists in the eastern and central regions, as well as first, second, and third tier cities, while for western regions and fifth tier cities, population mobility and increased in housing prices slows their economic development. There is no evidence of any spillover effect in fourth tier cities. A discussion and suggested policy implications are also provided.     

房价对居民消费的跨空间影响*——基于中国278个城市空间面板数据的实证研究

本文首先基于住房财富效应，构建了房价变动对居民消费的跨空间影响的分析框架。该框架阐释了住房财富效应的区域差异：不同地区间房价的空间传导、居民的异地消费以及消费的示范效应，使得本地房价变动可能引起周边地区居民消费的连锁反应。其后，本文利用中国278个地级市2000—2018年数据，通过空间面板杜宾模型，实证考察了不同地区住房财富效应的差异，以及房价变动对居民消费的跨空间影响。结果显示：（1）虽然中国整体上存在住房财富效应，但西部城市财富效应并不显著，并且核心城市还存在负财富效应。（2）不同城市间房价对消费存在跨区影响。东部城市和核心城市对周边城市消费的正向影响很显著，而边缘城市则对周边城市消费有显著的负向影响。（3）不同城市间的消费也具有空间上的示范效应。最后，本文从房价和居民消费空间联动的视角，为政府扩大内需、调控房地产市场提供了相关政策建议。     

Do your neighbors affect your bicycling choice? A spatial probit model for bicycling to The Ohio State University

Neighborhood social effects have recently become a focus of interest in transportation research, whereby transportation mode choice is not only affected by an individual’s characteristics and transportation system conditions, but also by the mode choices of that individual’s social neighbors. This study supports the neighborhood social effects argument, using a spatial econometrics approach and data from The Ohio State University (OSU) 2012 Campus Transportation Survey. A spatial probit model of commuters’ mode choices (bicycling versus non-bicycling) is estimated, accounting for spatial autocorrelation. The results show that the more OSU-affiliated bicycle riders are residing around an individual OSU commuter, the more attractive bicycling becomes, controlling for other factors such as gender, status, proximity to campus, bicycle infrastructure and attitudes. The results indicate that students and males are more likely to commute by bicycles. The probability of choosing bicycles decreases with distance from campus. In addition, proximity to bicycle infrastructure and physical environment both encourage respondents to bicycle. Feeling of safety, travel cost and concern for the environment also affect bicycling choice.     

城市扩张与集约农业背景下浅层地下水重金属污染特征空间分析

地下水是城乡结合部和农村地区的重要饮用水源之一,城市的扩张和农业集约经营对地下水的影响日益明显。本文以典型区—沈阳沈北新区为例,以地下水重金属为评价指标,采用经典统计和地统计的方法,分析城乡交错地区地下水污染程度和空间分异性特征,旨在为类似地区土地利用规划和地下水环境管理提供科学依据。结果显示:Fe、Mn超标率较高,分布范围较广;Cd、Hg分布集中,受城镇化的影响较为明显;Pb与集中养殖产业分布关系密切;As的分布与当地化工企业有关;Fe、Mn空间变异性强,空间异质性主要受自然条件等结构性因素控制;Cd、Pb、Hg空间变异性中等,As空间变异性弱,Cd、Pb、Hg、As这四种元素空间异质性的形成主要受人为活动等随机因素的影响;初步发现Fe和Mn,Mn和As有相似的来源。     

Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

The geography of suppliers and retailers

In the last decades, supply chains have increasingly transcended national boundaries developing into global supply chains. Along with the many opportunities arising from international sourcing and the extended commercial presence over the world, the management of a globally dispersed supply chain is highly complex. A key issue to consider when dealing with the global supply chain design is the location of facilities, not only with respect to firms' owned facilities but also the supply and distribution side as factors that affect supply chain complexity and operational performance. This paper sets out a methodological framework to characterize the geographical configuration of a firm's suppliers and retailer networks. Quantitative indexes of network spatial concentration and relative proximity measures based on a nonparametric kernel density estimator are developed to identify both intra- and inter-firm patterns between the supply and point of sales' distributions. The method is first described by means of a series of theoretical-illustrative examples and exemplified by analyzing the geographical dispersion of four practical cases from the fashion-textile industry (i.e., Adidas, Benetton, C&A, and Puma). Subsequently, managerial implications and potential use of the metrics are discussed, showing how the proposed approach can support researchers and practitioners to improve supply chain location decisions and logistic integration, and evaluate changes in either the purchasing or distribution strategy.     

Relationship between city size and firm productivity – A new interpretation using the Chinese experience

This paper contributes to the literature on the relationship between city size and firm productivity by focusing on agglomeration, selection (market competition), and sorting (presence of firms with diverse productivity) effects using Chinese firm-level data for 1998–2013. Contrary to the existing literature, our parametric regression estimates from nearly two million firms show that when the selection effect is controlled, productivity advantage in big cities is reversed. This outcome is explained through a quantile regression showing the existence of left-sided sorting (inefficient firms sort themselves to larger cities) in big cities which is not captured in existing empirical literature. We further find that (1) left-sided sorting is stronger in exporters than non-exporters; (2) is also generated mainly in enterprises with high asset-liability ratios; and (3) selection has a positive effect on firm productivity, suggesting that market competition is key in an explanation of the rapid growth of big cities in China.     

Spatial and operational factors behind passenger yield of U.S. nonhub primary airports

Nonhub airports are an essential component in the National Plan of Integrated Airport Systems (NPIAS) of the United States in that they connect regional towns and small communities to the air transportation network. Understanding the interplay of operational and spatial factors in determining average passenger yield of nonhub airports provides airlines with valuable information for network planning and revenue management. This study examines factors contributing to the yield variation among nonhub airports in the U.S. Using ordinary least squares (OLS) based econometric models, this study captures the spatial dependence of passenger yield of nonhub airports, which tends to increase with a corresponding increase in distance to the nearest large hub airport. Nonhub airports surrounding large hub airports with higher passenger enplanements and higher average yields also have higher yields than other nonhub airports. In addition, this study finds the effect of Allegiant Airlines in lowering the average passenger yield of the nonhub airports served directly by the airline, which can be termed as ‘Allegiant Effect’. Findings of this study could provide valuable guidance for airlines to analyze network planning strategies and to identify future markets for growth and for policymakers when allocating resources to communities relying on these nonhub airports.