Analysis of Spatial Autocorrelation in House Prices

This article examines spatial autocorrelation in transaction prices of single-family properties in Dallas, Texas. The empirical analysis is conducted using a semilog hedonic house price equation and a spherical autocorrelation function with data for over 5000 transactions of homes sold between 1991:4 and 1993:1. Properties are geocoded and assigned to separate housing submarkets within metropolitan Dallas. Hedonic and spherical autocorrelation parameters are estimated separately for each submarket using estimated generalized least squares (EGLS). We find strong evidence of spatial autocorrelation in transaction prices within submarkets. Results for spatially autocorrelated residuals are mixed. In four of eight submarkets, there is evidence of spatial autocorrelation in the hedonic residuals for single-family properties located within a 1200 meter radius. In two submarkets, the hedonic residuals are spatially autocorrelated throughout the submarket, while the hedonic residuals are spatially uncorrelated in the remaining two submarkets. Finally, we compare OLS and kriged EGLS predicted values for properties sold during 1993:1. Kriged EGLS predictions are more accurate than OLS in six of eight submarkets, while OLS has smaller prediction errors in submarkets where the residuals are spatially uncorrelated and the estimated semivariogram has a large variance.     

Predicting House Prices Using Multiple Listings Data

It is often necessary to accurately predict the price of a house between sales. One method of predicting house values is to use data on the characteristics of the area's housing stock to estimate a hedonic regression, using ordinary least squares (OLS) as the statistical technique. The coefficients of this regression are then used to produce the predicted house prices. However, this procedure ignores a potentially large source of information regarding house prices—the correlations existing between the prices of neighboring houses. The purpose of this article is to show how these correlations can be incorporated when estimating regression coefficients and when predicting house prices. The practical difficulties inherent in using a technique called kriging to predict house prices are discussed. The article concludes with an example of the procedure using multiple listings data from Baltimore.     

Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices

In this article different spatial statistics techniques to analyze the behavior of used dwelling market prices are compared. We fit two lattice models: simultaneous and conditional autoregressive, a geostatistical model, the so-called universal kriging and finally, a linear mixed-effect model. Different spatial neighborhood structures are considered, as well as different spatial weight matrices and covariance models. The results are illustrated through a real data set of 293 properties from Pamplona, Spain.     

Models for Spatially Dependent Missing Data

Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known characteristics. In fact, explanatory variable information on house characteristics for all properties, sold and unsold, are often available from assessors. We set forth an estimation approach that predicts missing values of the dependent variable when the sample data exhibit spatial dependence. Employing information on the housing characteristics of both sold and unsold properties can improve prediction, increase estimation efficiency for the missing-at-random case, and reduce self-selection bias in the non-missing-at-random case. We demonstrate these advantages with a Monte Carlo experiment as well as with actual housing data.     

金融业集聚对城镇化的影响及空间效应研究

依据全国31个省、市、自治区的省域空间面板数据,构建空间面板模型,考量金融业集聚对城镇化的影响及空间外溢效应。结果显示：我国省域城镇化率大部分集中在HH象限（高值集聚）和LL象限（低值集聚）,呈现较为明显的空间自相关性;全国层面,金融业集聚对城镇化的直接效应系数和间接效应系数均显著为正,说明金融业集聚对本省城镇化有显著的正向影响,且具有正向的空间外溢效应,能促进周边省份的城镇化;分东、中、西区域层面,东部地区金融业集聚对城镇化具有正向的影响,且具正向空间外溢效应,但在中西部地区,影响均不显著。基于此,应加强金融业集聚区建设,构建金融业集聚推进城镇化作用机制,在东中西区域科学布局建设金融中心。     

Spatial Statistics and Real Estate

Real estate has historically employed statistical tools designed for independent observations while simultaneously noting the violation of these assumptions in the form of clustering of same sign residuals by neighborhood, along roads, and near facilities such as airports. Spatial statistics takes these dependencies into account to provide more realistic inference (OLS has biased standard errors), better prediction, and more efficient parameter estimation. This article provides an overview of the field and directs readers to the relevant literature and software.     

Spatial Autocorrelations and Urban Housing Market Segmentation

This paper seeks to let data define urban housing market segments, replacing the conventional administrative or any pre-defined boundaries used in the previous housing submarket literature. We model housing transaction data using a conventional hedonic function. The hedonic residuals are used to estimate an isotropic semi-variogram, from which residual variance–covariance matrix is constructed. The correlations between hedonic residuals are used as identifier to assign housing units into clusters. Standard submarket identification tests are applied to each cluster to examine the segmentation of housing market. The results are compared with the prevailing structure of market segments. Weighted mean square test shows that the defined submarket structure can improve the precision of price prediction by 17.5%. This paper is experimental in the sense that it represents one of the first attempts at investigating market segmentation through house price spatial autocorrelations.     

Estimation employing a priori information within mass appraisal and hedonic pricing models

Both statistical appraisal and hedonic pricing models decompose houses into a set of individual characteristics. Regression estimates yield the contribution of each characteristic to total value. Unfortunately, straightforward application of OLS may produce untenable results such as implausible coefficient magnitudes or incorrect signs. Often the suspected cause is multicollinearity. This article examines the effect on estimation efficiency of differing levels of multicollinearity, R², and a priori information in the form of sub-market cost data, by comparing inequality restricted least squares (IRLS) with OLS in a series of Monte Carlo experiments. The IRLS procedure investigated here hybridizes the statistical market approach implemented by OLS, and the more traditional cost approach. The experiments show dramatic gains in estimation efficiency from exploiting a priori information through IRLS.     

运用大数据分析提升授信审批决策水平的思考

在经济新常态下,越来越多的企业进入结构调整和转型的新周期,给商业银行授信审批决策提出更高要求,而大数据时代的到来,为精准授信审批决策提供了有力支持.本文从大数据分析优势入手,通过大数据挖掘技术,进一步验证授信企业主背景及经营管理的真实性,提升授信企业风险的甄别能力,并对建立大数据完善授信审批决策机制提出相关建议.     

基于空间计量模型的金融发展与技术创新研究

通过运用空间计量经济学的空间滞后模型和空间误差模型,对2005-2009年的省际数据进行分析来检验金融发展与技术创新之间的内在关系。结果表明：在样本期内,各地区间的技术创新能力在空间上并非随机分布,而是呈现出显著的空间自相关性,且金融发展对技术创新有显著的促进作用;同时发现,股票市场的发展比银行信贷的增长更能促进技术创新水平的提高。     

Spatial Dependence and House Price Index Construction

Accurate estimation of prevailing metropolitan housing prices is important for both business and research investigations of housing and mortgage markets. This is typically done by constructing quality-adjusted house price indices from hedonic price regressions for given metropolitan areas. A major limitation of currently available indices is their insensitivity to the geographic location of dwellings within the metropolitan area. Indices are constructed based on models that do not incorporate the underlying spatial structure in housing data sets. In this article, we argue that spatial structure, especially spatial dependence latent in housing data sets, will affect the precision and accuracy of resulting price estimates. We illustrate the importance of spatial dependence in both the specification and estimation of hedonic price models. Assessments are made on the importance of spatial dependence both on parameter estimates and on the accuracy of resulting indices.     

Dwelling Age,Redevelopment, and Housing Prices: The Case of Apartment Complexes in Seoul

The aging of a housing structure not only leads to depreciation but also increases the possibility of redevelopment. If redevelopment accompanies an increase in structural density in order to accommodate the increased demand for housing, it provides large capital gains to the existing owners. In this case, expectations of redevelopment in the near future and the eventual announcement of redevelopment plans can have a strong positive impact on the current price of housing. We test this hypothesis using a hedonic pricing model designed to decompose the age effects into depreciation effect and redevelopment effect. Based on 3,474 observations on apartments in Seoul in 2001, estimation results confirm our hypothesis. While the depreciation effect dominates the redevelopment effect until 15 to 19 years of age, depending on the specification, the redevelopment effect eventually dominates the depreciation effect thereafter, causing the apartment price to increase. At 27 years of age, the apartment price decreases by as much as 4553 percent of the initial value, due to depreciation. However, the redevelopment effect increases the price by as much as 2832 percent, driving the price up to 7687 percent of the original value.     

Parametric,semiparametric, and nonparametric estimation of characteristic values within mass assessment and hedonic pricing models

Parametric estimators, such as OLS, attain high efficiency for well-specified models. Nonparametric estimators greatly reduce specification error but at the cost of efficiency. Semiparametric estimators compromise between these dual goals of efficiency and specification error. Semiparametric estimators can assume general forms within classes of functional forms. This paper applies OLS, the kernel nonparametric regression estimator, and the semi-parametric estimator of Powell, Stock, and Stoker (1989) to a data set, which should, based on theory and previous empirical work, yield positive coefficients. The semiparametric estimator, on average, displayed the performance most consistent with prior expectations followed by the nonparametric and parametric estimators. In addition, the paper shows how the semiparametric estimator can provide insights into the form of misspecification and suggest data transformations.     

Nonparametric methods with applications to hedonic models

Current real estate statistical valuation involves the estimation of parameters within a posited specification. Suchparametric estimation requires judgment concerning model (1) variables; and (2) functional form. In contrast,nonparametric regression estimation requires attention to (1) but permits greatly reduced attention to (2). Parametric estimators functionally model the parameters and variables affectingE(y¦x) while nonparametric estimators directly modelpdf(y, x) and henceE(y¦x).This article applies the kernel nonparametric regression estimator to two different data sets and specifications. The article shows the nonparametric estimator outperforms the standard parametric estimator (OLS) across variable transformations and across data subsets differing in quality. In addition, the article reviews properties of nonparametric estimators, presents the history of nonparametric estimators in real estate, and discusses a representation of the kernel estimator as a nonparametric grid method.     

The Influence of Riparian Protection Measures on Residential Property Values: The Case of the Oregon Plan for Salmon and Watersheds

The Oregon Plan for Salmon and Watersheds encourages residential property owners to plant riparian buffers in an effort to reduce stream temperature and thus improve fish habitat. This study estimates the change in the value of streamside residential properties in response to planting a treed riparian buffer. A hedonic pricing analysis suggests that treed riparian buffers reduce the market value of stream-front residential property in the study area.     

应用 Hedonic 估值方法的资本服务测算方式探究

学术界已有较多对于资本存量和资本服务核算的测量方法。文章通过 HV 估值方法使用差别化数据测算国内建筑类资本品的经验折旧,而不是使用以往所熟悉的永续盘存法。同时也给出了经济学文献中两种理论方法的对比研究：PIM 方法的若干假定与及 HV 方法使用市场价格的信息问题。回顾了资本服务测算的主要方面,即效率-年龄函数、折旧水平、指数问题与用户成本方法的应用前景。鉴于经济增长过程中资本服务相关性和动态变化,相应变量的准确测量是至关重要的。鉴于 HV 方法测算资本服务还处在探索和尝试阶段,文章尝试将资本存量与资本服务纳入统一框架体系,并就资本服务核算如何纳入国民账户体系以对我国资本项目进行测算精度研究。这一研究具有重要理论和实践意义。     

Simultaneous estimation of the demand and supply of differentiated audits

This study shows how the supply and demand for auditing services is analyzed in a simultaneous equations framework. An important aspect of the analysis is the assumption that an audit is a differentiated product that is valued for its productive attributes. A hedonic (multiattribute), nonlinear fee function defines the fees that clear the market for all audit packages traded. Analysis of marginal fees and quantities of audit attributes transacted requires simultaneous estimation of a supply and demand function for each attribute. Several approaches for estimating the hedonic fee function and achieving parameter identification in the underlying structural equations are suggested.     

Spatial statistical modelling of insurance risk: a spatial epidemiological approach to car insurance

Spatial models, such as the Besag, York and Mollie (BYM) model, have long been used in epidemiology and disease mapping. A common research question in these subjects is modelling the number of disease events per region; here the BYM models provides a holistic framework for both covariates and dependencies between regions. We use these tools to assess the relative insurance risk associated with the policyholders geographical location. A Bayesian modelling approach is presented and an elastic net is used to reduce the large number of possible geographic covariates. The final inference is performed using Integrated Nested Laplace Approximation. The model is applied to car insurance data from If P&C Insurance together with spatially referenced covariate data of high resolution, provided by Insightone. The entire analysis is performed using freely available R-packages. Including spatial dependence when modelling the number of claims significantly improves on the result obtained using ordinary generalised linear models. However, the support for adding a spatial component to the model for claims cost is weaker.     

The Evolution of Market Efficiency: 103 Years Daily Data of the Dow

Autocorrelation in daily returns of the Dow 30 Index fluctuates significantly over time and reveals a declining trend after World War II. The relation between autocorrelation and volatility is negative and nonlinear. The relation between autocorrelation and volume is also negative and nonlinear. Returns exhibit positive autocorrelation during years with higher autocorrelation, and negative autocorrelation during years with lower autocorrelation. Positive autocorrelation appears more frequently during periods of low volatility, while negative autocorrelation appears more frequently during periods of high volatility. Current period's autocorrelation is related to previous period's autocorrelation and to both the previous and the current period's volatility and rate of return, which implies that investors incorporate previous period's pattern of market behavior into their trading strategy.