Lectures on Public Economics

Evaluating the impact of public mass transit systems on real-estate values is an important application of the hedonic price model (HPM). Recently, a mathematical transformation of this approach has been proposed to account for the potential omission of latent spatial variables that may overestimate the impact of accessibility to mass transit systems on values. The development of a Difference-in-Differences (DID) estimator, based on the repeat-sales approach, is a move in the right direction. However, such an estimator neglects the possibility that specification of the price equation may follow a spatial autoregressive process with respect to the dependent variable. The objective of this paper is to propose a spatial Difference-in-Differences (SDID) estimator accounting for possible spatial spillover effects. Particular emphasis is placed on the development of a suitable weights matrix accounting for spatial links between observations. Finally, an empirical application of the SDID estimator based on the development of a new commuter rail transit system for the suburban agglomeration of Montreal (Canada) is presented and compared to the usual DID estimator.(This abstract was borrowed from another version of this item.)

A Spatial Difference-in-Differences Estimator to Evaluate the Effect of Change in Public Mass Transit Systems on House Prices

When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.

Take a Look Around: Using Street View and Satellite Images to Estimate House Prices

When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. 
We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. 
We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.

With the increasing volume and active transaction of real estate properties, mass appraisal has been widely adopted in many countries for different purposes, including assessment of property tax. In this paper, 104 papers are selected for the systematic literature review of mass appraisal models and methods from 2000 to 2018. The review focuses on the application trend and classification of mass appraisal and highlights a 3I-trend, namely AI-Based model, GIS-Based model and MIX-Based model. The characteristics of different mass appraisal models are analyzed and compared. Finally, the future trend of mass appraisal based on model perspective is defined as “mass appraisal 2.0”: mass appraisal is the appraisal procedure of model establishment, analysis and test of group of properties as of a given date, combined with artificial intelligence, geo-information systems, and mixed methods, to better model the real estate value of non-spatial and spatial data.

Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

Purpose




Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.




Design/methodology/approach




A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).




Findings




The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.




Originality/value




Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

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Examining the spatial relationship between environmental health factors and house prices: NO2 problem?

Geographically weighted regression (GWR) has been shown to greatly increase the performance of ordinary least squares-based appraisal models, specifically regarding industry standard measurements of equity, namely the price-related differential and the coefficient of dispersion (COD; Borst and McCluskey, 2008; Lockwood and Rossini, 2011; McCluskey et al., 2013; Moore, 2009; Moore and Myers, 2010). Additional spatial regression models, such as spatial lag models (SLMs), have shown to improve multiple regression real estate models that suffer from spatial heterogeneity (Wilhelmsson, 2002). This research is performed using arms-length residential sales from 2010 to 2012 in Norfolk, Virginia, and compares the performance of GWR and SLM by extrapolating each model's performance to aggregate and subaggregate levels. Findings indicate that GWR achieves a lower COD than SLM.(ProQuest: ... denotes formulae omitted.)IntroductionAd valorem property taxes are a prominent source of government revenue in jurisdictions around the world. Taxing authorities are held accountable to ensure that these valuations are fair and equitable. In such roles, the optimization of the accuracy of mass real estate valuation approaches is critical.Because of their precision and time- and cost-saving advantages, real estate mass appraisal methods that employ multiple regression-based models, known as automated valuation models (AVMs), are becoming increasingly prominent in industry practice and have received attention from the academic community. AVMs are used in a host of industries-both public and private- including loan origination, fraud detection, and portfolio valuation (Downie and Robson, 2007), and are promoted and advanced by such organizations as the International Association of Assessing Officers (IAAO). Statistical standards of equity established by such organizations give additional benchmarks by which modelers may test various approaches and methodologies.Academic research has expanded regression models using geographically specific dummy variables and distance coefficients, and, although this approach has been shown to improve ordinary least squares (OLS)-based regression models, they often still suffer from biased coefficients and t-scores (Berry and Bednarz, 1975; Fotheringham, Brunsdon, and Charlton, 2002; McMillen and Redfearn, 2010). Some researchers (Fotheringham, Brunsdon, and Charlton, 2002) have used geographically weighted regression (GWR), a locally weighted regression technique, which has improved model performance by employing a spatial weighting function and allowing for coefficients to fluctuate across geographic space (Huang, Wu, and Barry, 2010; LeSage, 2004). Similarly, the spatial lag model (SLM)-a spatial autoregressive (SAR) model- addresses spatial heterogeneity by including an autocorrelation coefficient and spatial weights matrix (Anselin, 1988).Because real estate markets behave differently across geographic space, AVMs free of spatial consideration often produce inaccurate, misleading results (Anselin and Griffith, 1988; Ball, 1973; Berry and Bednarz, 1975). GWR is prominently demonstrated throughout literature as a more accurate alternative to multiple regression analysis (MRA) AVMs (for example, Borst and McCluskey, 2008; Lockwood and Rossini, 2011; McCluskey et al., 2013; Moore, 2009; Moore and Myers, 2010). Similarly, SAR models have been sufficiently demonstrated to increase the predictive accuracy of such models (Borst and McCluskey, 2007; Conway et ah, 2010; Quintos, 2013; Wilhelmsson, 2002). Descriptions of their methods and findings are summarized in exhibit 1.Despite the popularity of both GWR and SLM models in housing research, to our knowledge, a study that simultaneously compares the performance of GWR and SLM using industry-accepted IAAO standards and that extrapolates each model's performance to aggregate and subaggregate levels has yet to be published. …

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Evaluating Spatial Model Accuracy in Mass Real Estate Appraisal: A Comparison of Geographically Weighted Regression and the Spatial Lag Model

This Article is brought to you for free and open access by the School of Public Service at ODU Digital Commons. It has been accepted for inclusion in School of Public Service Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact digitalcommons@odu.edu. Repository Citation Bidanset, Paul E. and Lombard, John R., "The Effect of Kernel and Bandwidth Specification in Geographically Weighted Regression Models on the Accuracy and Uniformity of Mass Real Estate Appraisal" (2014). School of Public Service Faculty Publications. 27. https://digitalcommons.odu.edu/publicservice_pubs/27

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The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal

Research has consistently demonstrated that geographically weighted regression (GWR) models significantly improve upon accuracy of ordinary least squares (OLS)-based computer-assisted mass appraisal (CAMA) models by more accurately accounting for the effects of location (Fotheringham et al. 2002; LeSage 2004; Huang et al. 2010). Bidanset and Lombard (2014a, 2017) previously studied the impacts of various kernel and bandwidth combinations employed in building residual (i.e. sale price less land value) GWR CAMA models and found that the specification of each does bear significant effect on valuation equity attainment. This paper builds upon the previous research by comparing performance of weighting specifications of non-building residual (i.e. full sale price) GWR CAMA models using new data of a different geographic real estate market. We find that the exponential kernel and fixed bandwidth together achieve a superior COD for our data, and that COD does fluctuate depending on the GWR weighting specification.

Further Evaluating the Impact of Kernel and Bandwidth Specifications of Geographically Weighted Regression on the Equity and Uniformity of Mass Appraisal Models

Purpose




Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is problematic to understand the nature of the relationship between house prices and the accessibility to services and prominent neighbourhood landmarks and amenities. Therefore, this paper aims to quantify and measure the (dis)amenity effects on house pricing levels within particular geographic housing sub-markets.




Design/methodology/approach




Most hedonic models are estimated using regression techniques which produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price. This paper uses a quantile regression (QR) approach that provides a “more complete” depiction of the marginal impacts for different quantiles of the price distribution using sales data obtained from 3,780 house sales transactions within the Belfast Housing market over 2014.




Findings




The findings emerging from this research demonstrate that housing and market characteristics are valued differently across the quantile values and that conditional quantiles are asymmetrical. Pertinently, the findings demonstrate that ordinary least squares (OLS) coefficient estimates have a tendency to over or under specify the marginal mean conditional pricing effects because of their inability to adequately capture and comprehend the complex spatial relationships which exist across the pricing distribution.




Originality value




Numerous studies have used OLS regression to measure the impact of key housing market externalities on house prices, providing a single estimate. This paper uses a QR approach to examine the impact of local amenities on house prices across the house price distribution.

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Paul Bidanset

Papers

Examining the spatial relationship between environmental health factors and house prices: NO2 problem?

Evaluating Spatial Model Accuracy in Mass Real Estate Appraisal: A Comparison of Geographically Weighted Regression and the Spatial Lag Model

The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal

Further Evaluating the Impact of Kernel and Bandwidth Specifications of Geographically Weighted Regression on the Equity and Uniformity of Mass Appraisal Models

House prices and neighbourhood amenities: beyond the norm?