220 research outputs found

    THE INFLUENCE OF WATER QUALITY ON THE HOUSING PRICE AROUND LAKE ERIE

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    The primary objective of this paper is to estimate the influence of Lake Erie water quality on the housing price by taking spatial effects into account. The robust LM tests for spatial autocorrelation suggested that spatial error model specification is more likely model in our study. Fecal coliform counts and Secchi depth disk reading are used as water quality measures. In order to overcome the spatio-temporal aspects of Secchi depth disk reading data, Kriging was used for spatial prediction. We found the significant influences of both water quality measures on housing values. Gradient effects considering the distance from a beach and water quality variables are also observed.Public Economics,

    Interpolation methods for geographical data: Housing and commercial establishment markets

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    The estimation of commercial property prices in a touristic city can be explored through spatial interpolation methods, but in the presence of small sample sizes, auxiliary stochastic processes that are correlated with the prices of commercial establishments are needed. The aim of this paper is to compare the various estimates of commercial establishment prices in Toledo (Spain) provided by methods based on inverse distance weighting, 2-D shape functions for triangles, kriging and cokriging (the housing prices being the auxiliary stochastic process). The results indicate that kriging improves the classical interpolation methods and that cokriging has a clear advantage over kriging.

    Bayesian and Frequentist Approaches to Hedonic Modeling in a Geo-Statistical Framework

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    We compare Least Squares, Maximum Likelihood and Bayesian approaches to estimation in a Hedonic context. The approaches are compared from theoretical and practical perspectives and from the viewpoint of a policy maker or urban planner. The approaches are applied to data on the property market in Bogota, Colombia. We find that no approach is unambiguously better than the others and recommend that choice of estimation technique should be predicated upon the characteristics of the policy problem at hand.Research Methods/ Statistical Methods,

    Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging

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    Cokriging is a multivariate spatial method to estimate spatial correlated variables. This method allows spatial estimations to be made and interpolated maps of house price to be created. These maps are interesting for appraisers, real estate companies, and bureaus because they provide an overview of location prices. Kriging uses one variable of interest (house price) to make estimates at unsampled locations, and cokriging uses the variable of interest and auxiliary correlated variables. In this paper, housing location price is estimated using kriging methods, isotopic data cokriging, and heterotopic data cokriging methods. The results of these methods are then compared.

    Geographical and temporal weighted regression (GTWR)

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    Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19-year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling

    Impact of Air Pollution on Property Values: a Hedonic Price Study

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    The main purpose of this study is the calculation of implicit prices of the environmental level of air quality in Yogyakarta on the basis of housing property prices. By means of Geographical Information System, the housing property prices characterized from the area which have highest air pollution level in province of Yogyakarta. Carbon monoxide is used as the pollution variable. The methodological framework for estimation is based on a hedonic price model. This approach establishes a relationship between the price of a marketable good (e.g. housing) and the amenities and characteristics this good contains. Therefore, if variations in air pollution levels occur, then households would change their behavior in an economic way by offering more money for housing located in highly improved environmental areas. The hedonic regression results that the housing price decrease while increasing the level of air contamination such substance as carbon monoxide

    Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods

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    This paper compares alternative methods for taking spatial dependence into account in house price prediction. We select hedonic methods that have been reported in the literature to perform relatively well in terms of ex-sample prediction accuracy. Because differences in performance may be due to differences in data, we compare the methods using a single data set. The estimation methods include simple OLS, a two-stage process incorporating nearest neighbors’ residuals in the second stage, geostatistical, and trend surface models. These models take into account submarkets by adding dummy variables or by estimating separate equations for each submarket. Based on data for approximately 13,000 transactions from Louisville, Kentucky, we conclude that a geostatistical model with disaggregated submarket variables performs best.

    Leveraging geospatial statistics for measuring and valuing the urban environment

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    This thesis looks at emerging uses of geospatial data for analysing the urban environment. As high-dimensional data becomes increasingly available, sophisticated spatial and temporal statistical estimation strategies can assess the minutia of environmental processes in a dynamic urban context. Each essay focuses on the improved measurement of high-resolution non-market environmental amenities and evaluating them using observed impacts on house prices or transportation networks. While valuation techniques for each amenity vary depending on context, these works all highlight a set of spatial methodologies for detailed urban analytics with a particular focus on urban greenery, seismic and flood risk, and pollution mitigation
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