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Hedonic, Residual, and Matching Methods for Residential Land Valuation
Abstract
Accurate estimates of land values on a property-by-property basis are an important requirement for the effective implementation of land-based property taxes. We compare hedonic, residual, and matching techniques for mass appraisal of residential land values, using data from Maricopa County, Arizona. The first method involves a hedonic valuation model estimated for transactions of vacant lots. The second approach subtracts the depreciated cost of improvements from the value of improved properties to obtain land value as a residual. The third approach matches the sales of vacant lots with subsequent sales of the same properties once they have been developed. For each pair, we use a land price index to inflate the land price to the time of the improved property transaction and then calculate land leverage (the ratio of land to total property value). A hedonic model is estimated and used to predict land leverage for all improved properties. We conclude that the matching approach is the most promising of the methods considered.
... Persyaratan penting dalam penentuan pajak tanah properti adalah nilai jual objek bumi dan bangunan (Bourassa & Hoesli, 2022). Besarnya nilai tanah dipengaruhi oleh beberapa faktor, seperti karakteristik fisik, lokasi, dan lingkungan (Sutawijaya, 2004;Astrisele & Santosa, 2019). ...
Meningkatnya harga properti di kawasan perkotaan dipengaruhi oleh pesatnya urbanisasi di kawasan perkotaan. Pola spasial terkait nilai tanah dan properti yang kompleks akibat pembangunan gedung-gedung tinggi belum dipahami dengan baik. Secara umum, penilaian tanah dan properti dipengaruhi oleh aspek lokasi, lingkungan, dan fisik. Pembangunan yang terus berkembang menyebabkan perubahan spasial yang signifikan dalam dimensi vertikal. Faktor 2D yang saat ini digunakan tidak dapat mewakili perubahan informasi spasial pada dimensi vertikal. Oleh karena itu, penting untuk memperhitungkan faktor 3D dalam proses penilaian tanah dan properti. Penelitian ini bertujuan untuk menganalisis faktor 3D pada penilaian tanah dan properti sebagai dasar penentuan pajak dengan menerapkan metode Hedonic Price Modeling (HPM). Metode HPM dikembangkan untuk penilaian tanah dan properti 3D menggunakan regresi berganda dengan pembelajaran mesin (machine learning). Hasil dari penelitian, menunjukkan bahwa penambahan faktor 3D pada model memiliki pengaruh yang signifikan terhadap nilai tanah dan properti. Hal ini didukung oleh hasil validasi menggunakan uji independen t, yang menunjukkan bahwa tidak terdapat perbedaan secara signifikan antara rata-rata nilai pasar dan Nilai Jual Objek Pajak (NJOP). Selain itu, penyajian model kota LoD 1, dinilai efektif dalam menggambarkan pola distribusi spasial terkait klasifikasi Pajak Bumi dan Bangunan (PBB) yang tersebar di area studi. Pola yang tergambar dalam model kota tersebut dapat mempermudah pemangku kepentingan dalam memahami tren PBB dalam konteks spasial, sekaligus memudahkan dalam pengambilan keputusan terkait kebijakan pajak dan perencanaan kota.
... As a result, researchers often resort to methodologies like Willingness-To-Pay -Willingness-To-Accept (WTP-WTA) and hedonic models in isolation. Although some studies have explored the Greek real estate sector, the application of internationally recognized methodologies such as hedonic and geographically weighted regression models (Goodman 1998;Fotheringham et al. 2002;Abidoye and Chan 2017;Bourassa and Hoesli 2022) remains primarily confined to academic research. The influence of specific environmental and urban factors has been studied individually in the past using simpler correlation methods and later with the assistance of hedonic models. ...
Urban regenerations have always been a tool towards the improvement of the urban environment through environmental integration and thereafter the quality of life of citizens. However, all regenerations do not have the same success and impact on the surrounding properties. In this paper, two major urban regenerations of Thessaloniki, the second largest city of Greece, are studied through the scope of their effect on property values in combination with the analysis of the effect of traditional property characteristics on values. This study was necessary to fill the gap of determining that sustainable urban planning and environmental integration are nowadays proven more important than property characteristics, when population gathering in urban areas is at its highest point. The current study applied geographically weighted regression, spatial autocorrelation and hot-spot analysis through the environment of ArcGIS to determine which are the factors that citizens seek in their residential area and if these factors are more important than their property characteristics. The findings of the study indicate that citizens always seek for additional urban green and urban quality even in areas where the urban environment is already at a very good level. The difference appears in the fact that in areas where urban green or open spaces are upsent, the weight leans on the characteristics of the wider area and its citizens (socio-economic characteristics, habits etc.) rather than on the property characteristics, when determining property values. On the other hand, citizens living in areas with urban green and open spaces, move a step forward and seek for larger and more friendly green or regenerated areas as a counterpoint for already increased property values. In general, the result of this research underlines that a targeted urban planning for each sub-area is one of the keys toward property values stabilization against economic variations and towards the improvement of the quality of citizens’ lives through environmental integration. Therefore, decision makers and urban designers should take into account all different needs of the citizens in each area setting the goal of maximum possible urban sustainability and resilience and the minimum environmental degradation.
... Research method. The Hedonic Price Method (HPM) is an essential approach for studying property prices (Bourassa and Hoesli, 2022 commonly used in economic analyses related to housing (Muehlenbachs et al., 2015). Harding et al. (2003) further incorporated personal factors of buyers and sellers into the HPM. ...
Trust is an important social capital and informal institution that influences transaction behaviors, especially in the second-hand housing market of China. This study analyzes the second-hand housing transactions of 17 cities and matches the birthplaces of these traders with CESS2000 to measure the degree of trust in each transaction. Then we use the combined dataset to examine the impact of the degree of trust on the bargaining power during the negotiation process. The empirical results indicate that there is a significant positive correlation between the degree of trust and negotiation rate, and buyers are more likely to capture trust premium. Moreover, “youth capital” emphasizes the role of trust in price bargaining during the second-hand housing transactions. Local advantage has a substitution effect on trust, which means it is an alternative explanation of trust affecting price bargaining during the second-hand housing transactions. Furthermore, the impact of trust varies by gender with male traders being more affected than female traders. All in all, this study provides practical implications of trust premium in housing transactions, and policy implications of establishing a social credit system. Governments can reduce transaction friction and transaction costs by establishing an appropriate formal institution, such as a personal credit database. As research on trust premium affecting micro-behaviors in the housing market is scarce, this study aims to fill this gap.
Studying spatial‐temporal change in residential land prices (RLP) can help implement real estate regulations to promote the healthy development of the residential land market. This study applied the urban scaling law to explore the spatial‐temporal pattern of RLP from 2010 to 2019 in China. We found rapid growth in RLP across China from 2010 to 2019, with a growth rate of 179.12%. Among them, the Eastern Region had the highest RLP and growth rates. Furthermore, differences in RLP between large cities and small‐medium cities widened, indicating a trend of the Matthew effect in prefecture‐level cities. Moreover, the scale‐adjusted metropolitan indicator (SAMI) indicated urban agglomerations including Beijing‐Tianjin‐Hebei (BTH), Yangtze River Delta (YRD), Guangdong‐Fujian‐Zhejiang Coastal (GFZ), and Pearl River Delta (PRD) located in the Eastern Region had higher RLP than urban agglomerations with the same population size. In addition, the SAMI also highlighted disproportionately high RLP in some small‐medium cities compared to equivalent cities, warranting regulatory policy attention.
Data from millions of appraisals in 2012-2019 are used to estimate residential land prices, the share of house value attributable to land, and related statistics down to the census-tract level for areas that include the vast majority of U.S. population and single-family housing. The results confirm predictions about land prices from canonical urban models. Over 2012-2019, we show that land prices rose faster than house prices in large metro areas, boosting the land share of house value, while the land share fell in smaller metros. The data are available for download at https://www.fhfa.gov/papers/wp1901.aspx.
Land plays a critical role in both economic and environmental accounting. As an asset, it occupies a unique position at the intersection of the System of National Accounts (SNA), the System of Environmental-Economic Accounting Central Framework (SEEA-CF), and (as a spatial unit) SEEA Experimental Ecosystem Accounting (SEEA-EEA), making land a natural starting point for developing natural capital accounts more generally. We develop a pilot set of national and subnational land accounts for the United States that are consistent with the SEEA-CF and SNA principles, quantified in both physical and monetary terms. The physical accounts utilize detailed land use (National Land Use Database) and land cover (National Land Cover Database) datasets, which provide insights into how land cover in the U.S. is changing over time. To provide aggregate estimates of land values, we use a hedonic approach that exploits fine-grain microdata (“big data” from Zillow) that contains detailed information from hundreds of millions of property transactions and their corresponding physical characteristics covering much of the U.S. Methodologically, we show that it is feasible to produce monetary accounts for land that can be directly linked to and integrated with physical land cover/use. Overall, U.S. land cover has shown declines in forests, cropland, and pasture with increases in barren, scrub/shrub, and developed classes, which are particularly concentrated in the U.S. Southeast. Nominal land values in the U.S. fell about 28% (25.1 trillion in 2016.
The paper uses hedonic regression techniques in order to decompose the price of a house into land and structure components using readily available real estate sales data for a Dutch city. In order to get sensible results, it was useful to use a nonlinear regression model using data that covered multiple time periods. It also proved to be necessary to impose some restrictions on the price of structures. The resulting builder’s hedonic regression model was compared with the results for traditional logarithmic hedonic regression models.
Few studies have analyzed the determinants of house value volatility at the level of individual houses. This paper uses two panels of the American Housing Survey covering 1974-2003 to test four hypotheses related to the determinants of house value volatility. The findings are that 1) house values at both ends of the quality distribution have greater variance than those with average quality levels, 2) the more atypical a house is, the greater the variance of house value, 3) the more highly "land leveraged" a house is, the greater the variance of its value, and 4) house values owned by minority households have greater variance than those of whites.
The absolute location of each real estate parcel in an urban housing market has a unique location-value signature. Accessibility indices, distant gradients and locational dummies cannot fully account for the influence of absolute location on the market price of housing because there are an indeterminable number of externalities (local and nonlocal) influencing a given property at a given location. Furthermore, the degree to which externalities affect real estate values is not only unique at each location but highly variable over space. Hence, absolute location must be viewed as interactive with other determinants of housing value. We present an interactive variables approach and test its ability to explain price variations in an urban residential housing market. The statistical evidence suggests that the value of location, as embodied in the selling price of housing units, may not be separable from other determinants of value. It is recommended that housing valuation models, therefore, be specified to allow site, structural and other independent attributes to interact with absolute location-{x, y} coordinates-when accounting for intraurban variation in the market price of residential housing. This approach is especially useful when estimating the value of housing for geographic areas where very little is known a priori about the neighborhoods or submarkets. Copyright 2003 by the American Real Estate and Urban Economics Association
Purpose
The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.
Design/methodology/approach
The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression, random forests, gradient boosting and neural networks) and two updating methods (moving and extending windows). They use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017.
Findings
The gradient boosting method yields the greatest accuracy, while the robust method provides the least volatile predictions. There is a clear trade-off across methods depending on whether the goal is to improve accuracy or avoid volatility. The choice between moving and extending windows has only a modest effect on the results.
Originality/value
This paper compares a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but which have not been considered in prior research. The techniques include robust regression, which has not previously been used in this context. The data updating allows for analysis of the volatility in addition to the accuracy of predictions. The results should prove useful in improving hedonic models used by property tax assessors, mortgage underwriters, valuation firms and regulatory authorities.
This study explores the use of “teardown” sales to estimate the value of urban land. When a buyer purchases a property intending to tear down the existing structure and rebuild, the value of land can potentially be estimated as the purchase price plus demolition costs. There has been little exploration of teardown sales in cities around the country, or any explicit comparisons between the estimates of land values derived from teardown sales and those derived through vacant land sales. This paper undertakes just such an explicit comparison, analyzing approximately 3800 teardown sales and 4900 vacant land sales occurring in New York City between 2003 and 2009. The two approaches yield surprisingly similar estimates of the value of both parcel attributes and locational amenities. However, vacant parcels are disproportionately located in very distressed neighborhoods and tend to be valued less highly than teardown parcels, even in the same neighborhood. Teardown parcels appear to be more representative of the city as a whole and may be a more useful approach to developing estimates of land prices, at least in the central cities of large urban areas where sample sizes are large enough.
Despite unprecedented vertical growth in large cities, the economics of skyscrapers remain understudied. We combine data on tall buildings with a panel of land prices covering 140 years to analyze the determinants of urban heights. We provide estimates of the land price elasticity of height, the height elasticity of construction cost, and the elasticity of substitution between land and capital for tall buildings. The land price elasticity of height increased substantially over time, and it is larger for commercial than for residential buildings, which suggests that the supply side helps to produce the typical segregation of urban land uses. © 2018 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
We use a new property-level data set and an innovative methodology to estimate the price of land from 2000 to 2013 for nearly the universe of detached single-family homes in the Washington, DC metro area and to characterize the boom-bust cycle in land and house prices at a fine geography. The results show that land prices were more volatile than house prices everywhere, but especially so in the areas where land was inexpensive in 2000. We demonstrate that the change in the land share of house value during the boom was a significant predictor of the decline in house prices during the bust, highlighting the value of focusing on land in assessing house-price risk.
How have house prices evolved over the long run? This paper presents annual house prices for 14 advanced economies since 1870. We show that real house prices stayed constant from the nineteenth to the mid-twentieth century, but rose strongly and with substantial cross-country variation in the second half of the twentieth century. Land prices, not replacement costs, are the key to understanding the trajectory of house prices. Rising land prices explain about 80 percent of the global house price boom that has taken place since World War II. Our findings have implications for the evolution of wealthto- income ratios, the growth effects of agglomeration, and the price elasticity of housing supply.
This study draws from the redevelopment, real option, and urban spatial growth literatures to explore the spatial dynamics of the components of house prices. More specifically, the paper proposes that the capitalized value of the option to redevelop housing at the property level can be estimated by incorporating the likelihood of exercising the redevelopment option (the probability of redevelopment) into spatial and non-spatial hedonic house price models. Accordingly, option values are estimated for properties across the spectrum of the housing life cycle. Results from the study reveal a substantial level of spatial variation and clustering in the predicted option values, indicating that location is a major determinant of redevelopment and real option values. Furthermore, the results provide new evidence in support of the theoretical construct that properties purchased for immediate redevelopment are only valued for the underlying land. This article is protected by copyright. All rights reserved
Estimating the market value of vacant land is one of the more difficult tasks of current real estate appraisal. The problem
applies equally to the appraisal of a single site or to the mass appraisal of land. Appraisal texts list several methods to
use for this purpose. Regardless of the method chosen, however, when land sales are sparse, valuation may be reduced to an
educated guess.
Although the average change in house prices is related to changes in fundamentals or perhaps market-wide bubbles, not all houses in a market appreciate at the same rate. The primary focus of our study is to investigate the reasons for these variations in price changes among houses within a market. We draw on three theories for guidance, one related to the optimal search strategy for sellers of atypical dwellings, another focusing on the bargaining process between a seller and potential buyers and the third relying on the concept of land leverage. We hypothesize that houses will appreciate at different rates depending on the characteristics of the property and the change in the strength of the housing market. These hypotheses are supported using data from three New Zealand housing markets.
A house is a bundle of land and improvements, with the weights of the two components varying both over time and across locations. We capture the land intensity or “leverage” of a property by measuring the ratio of land to total value. This is accomplished using transactions data for single-family homes in Switzerland over the period 1978 to 2008. We show how to use hedonic models to develop time series of land prices and land leverage. Then we estimate error correction models for both house prices and land leverage. We show the importance of interacting land leverage with fundamentals when assessing the determinants of house prices. House price changes are shown to be affected by changes in real construction costs, in real GDP per capita, and in the growth of the population aged 30 to 49, while land leverage changes are a function of changes in real construction costs and in real GDP per capita.
We maintain that the appropriate definition of submarkets depends on the use to which they will be put. For mass appraisal purposes, submarkets should be defined so that the accuracy of hedonic predictions will be optimized. Thus we test whether out-of-sample hedonic value predictions can be improved when a large urban housing market is divided into submarkets and we explore the effects of alternative definitions of submarkets on the accuracy of predictions. We compare a set of submarkets based on small geographical areas defined by real estate appraisers with a set of statistically generated submarkets consisting of dwellings that are similar but not necessarily contiguous. The empirical analysis uses a transactions database from Auckland, New Zealand. Price predictions are found to be most accurate when based on the housing market segmentation used by appraisers. We conclude that housing submarkets matter, and location plays the major role in explaining why they matter.
An empirical model of residential housing demolitions and the redevelopment process in the Chicago metropolitan area supports the theoretical prediction that the sales price of teardown property is approximately equal to its land value. We control for sample selection bias associated with non-random demolition permit applications using a probit model that accounts for misclassification of the dependent variable. The probit models provide statistically significant evidence of misclassification, and suggest that prime teardown candidates are small, older, homes near public transportation and traditional village centers.
The rate at which physical capital depreciates is fundamental to investment in the economy. Nevertheless, although housing capital accounts for one-third of the total capital stock, the rate at which housing capital depreciates has only rarely been directly estimated, in part because prior studies do not control for maintenance. For that same reason, widely publicized measures of house price appreciation overstate the capital gain from homeownership.Using data from the American Housing Survey we examine these issues. Over the 1983 to 2001 period, results indicate that gross of maintenance, housing depreciates at roughly 2.5 percent per year, while net of maintenance, housing depreciates at approximately 2 percent per year. Moreover, although the typical home appreciated at an annual real rate of roughly 0.75 percent, after allowing for depreciation and maintenance, the average homeowner experienced little capital gain.
Combining data from several sources, we build a database of home values, the cost of housing structures, and residential land values for 46 large US metropolitan areas from 1984 to 2004. Our analysis of these new data reveal that since the mid-1980s residential land values have appreciated over a much wider range of cities than is commonly believed. And, since 1998, almost all large US cities have seen significant increases in real residential land prices. Averaging across the cities in our sample, by year-end 2004, the value of residential land accounted for about 50 percent of the total market value of housing, up from 32 percent in 1984. An implication of our results is that housing is much more land intensive than it used to be, meaning that the future course of home prices—the average rate of appreciation and volatility—is likely to be determined even more by demand factors than was the case even ten or twenty years ago.
This paper presents the first careful test of an equilibrium model of urban redevelopment. In addition, it demonstrates a new methodology for recovering vacant urban land prices in areas that have already been developed. Using data on single family detached homes sold in Vancouver, B. C., we estimate the price of vacant land based on properties that were sold and redeveloped. The price of developed land is also estimated based on properties that were sold and not redeveloped. Various tests overwhelmingly support the hypothesis that housing is redeveloped when the price of vacant land exceeds the price of land in its current use. This result provides support for various theoretical models of urban spatial growth and indicates that one can recover vacant urban land prices by examining redeveloped properties.
This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators--intermediaries between sample mean and sample median--that are asymptotically most robust (in a sense to be specified) among all translation invariant estimators.
This paper reports new estimates of the elasticity of substitution between land and non-land inputs in the production of housing. Errors in the measurement of land value may have biased existing estimates of the substitution elasticity downward, leading to the conclusion that the substitution elasticity is less than one. The data analyzed in this study consist of observations on the transaction prices of both the lot and house, obtained by tracking the development of residential subdivisions. The results indicate that the substitution elasticity is higher than indicated in previous studies, and may not differ significantly from one.
The assessment requirements for a separate tax on land
- Bell
Swings in commercial and residential land prices in the United States
- Nichols
Spatial econometrics
- Anselin
Land value taxation in light of current assessment theory and practice
- Back
Robust repeat sales indexes
- Bourassa
Land leverage: decomposing home price dynamics
- Bostic
Robust hedonic price indexes
- Bourassa
Estimating residential land value by multivariate analysis
- Downing
Smearing estimate: a nonparametric retransformation method
- Duan
The relevance and feasibility of land value taxation in the rich countries
- Netzer
Land value taxation: administrative feasibility—retrospect and prospect
- Welch