1The Hedonic Price Method in Real Estate and Housing Market Research: A2Review of the Literature345Herath, Shanaka; Maier, Gunther67DOI:810.57938/e55da0fe-d130-415d-9a5d-7bbc07c329a19101112Published: 01/01/2010131415Document Version:16Publisher's PDF, also known as Version of record171819Document License:20Unspecified2122232425Link to publication2627282930Citation for published version (APA):31Herath, S., & Maier, G. (2010). The Hedonic Price Method in Real Estate and Housing Market Research: A32Review of the Literature. WU Vienna University of Economics and Business. SRE - Discussion Papers No.332010/03 https://doi.org/10.57938/e55da0fe-d130-415d-9a5d-7bbc07c329a13435363738Download date: 20. 10月 202639 Institut für Regional- und Umweltwirtschaft40Institute for the Environment and Regional Development4142434445 Shanaka Herath, Gunther Maier4647484950 The Hedonic Price Method in Real Estate and51 Housing Market Research: A Review of the52 Literature5354555657 SRE-Discussion 2010/03 201058 The hedonic price method in real estate and housing market59 research: a review of the literature6061 Shanaka Herath, Gunther Maier6263 Research Institute for Spatial and Real Estate Economics64 Vienna University of Economics and Business65 Austria666768 Abstract6970 The Hedonic Price Method (HPM), also known as hedonic regression, is used71 for estimating the value of a commodity or the demand for a commodity. The72 HPM has been extensively used in real estate and housing market research in73 the recent past. In this paper, we discuss theoretical and methodological74 developments related to hedonic regression and undertake an examination of75 use of this methodology in the recent real estate and housing literature. We76 first define the HPM, and explain the fundamentals behind the methodology.77 The idea behind the HPM is that the commodities are characterized by their78 constitute properties, hence the value of a commodity can be calculated by79 adding up the estimated values of its separate properties. In the second part of80 the paper, we emphasise that the heterogeneous nature of real estate property81 justifies the use of HPM for estimating their value or demand. We also take a82 stock of most cited empirical studies on real estate and housing using the83 HPM, and classify those into several categories. The classification indicates84 that neighbourhood characteristics of real estate are relatively over-researched85 as a determinant of price or rent. It also shows that implicit value of structural86 characteristics is under-researched. In general, implicit value of environmental87 amenities in the neighbourhood and air pollution are relatively over-88 researched. The effect of social factors, i.e. racial segregation and crimes on89 real estate value is under-researched.9091921. Introduction9394The hedonic price method (hereafter, HPM) is also known as the hedonic demand95theory or hedonic regression. This methodology estimates the value of the96characteristics of a commodity that indirectly affects its market price. Alternatively it97is used for estimating the demand for a commodity. The HPM is used in consumer98and market research (e.g. Hirschman and Holbrook, 1982), calculation of consumer99price indices (e.g. Moulton, 1996), tax assessment (e.g. Berry and Bednarz, 1975),100valuation of cars (e.g. Cowling and Cubbin, 1972), computers (e.g. White et al, 2004)101etc. in addition to real estate economics and real estate appraisal, the topic we discuss102in this paper. The methodology has recently been used extensively in real estate and103104105 1106 housing market research: some of the most applied areas include correction for quality107changes in constructing a housing price index, assessment of the value of a property108in the absence of specific market transaction data, analysis of demand for various109housing characteristics or housing demand in general, and testing assumptions in110spatial economics.111112The fundamental idea of the HPM is as follows: commodities are characterized by113their constitute properties, hence the value of a commodity can be calculated by114adding up the estimated values of its separate properties. According to this informal115definition, a couple of requirements need to be fulfilled in order to be able to calculate116hedonic prices. Those are that the composite good under consideration could be117reduced to its constituent parts and there is an implicit value for those constituent118parts in the market.119120In the first part of the paper, we discuss fundamentals behind the HPM. We continue121with a discussion of theoretical and methodological developments related to hedonic122regression. The latter part of our paper undertakes an examination of areas of123empirical studies in the recent real estate and housing literature using the HPM. The124research question here is whether some topics are over-researched and some are125under-researched in recent real estate and housing research.126127This paper begins discussing definitions of hedonic price method as presented in128previous literature. Section 2 provides a brief overview of historical developments129related to the methodology. Section 3 looks at different estimation techniques and130issues related to functional form and model specification of popular HPM models.131Section 4 evaluates various contributions encountered in our classification of132empirical studies and section 5 concludes the paper by providing a summary of our133survey findings.1341352. A brief historical account of the hedonic price method136137There is no consensus among scholars as to who first introduced the method of138hedonic regression even though most of the scholars agree that it was Court (1939)139who first used the HPM. Accordingly, Bartik (1987), Goodman (1998), Robert and140Shapiro (2003) among many others argue that the first actual estimation of a hedonic141142143144 2145 price model was a hedonic price index for automobiles by A.T. Court (1939). These146scholars document that the methodology was popularised by Zvi Griliches in the early1471960s.148149Court (1939) states that passenger cars serve many diverse purposes and suggests to150combine several specifications to form a single composite measure in price index151procedures. He went on to use the term ‘hedonic’ for the first time, to explain the152weighting of the relative importance of the various components including horsepower,153braking capacity, window area, seat width, and tire size. One reason to consider154Court’s study as a significant contribution is that it deals with problems of non-155linearity and with changes in underlying goods bundles (Goodman, 1998).156157Robert and Shapiro (2003), commenting on Court’s methodology, contend that158“…implicit price components for each of a bundle of product characteristics are159determined by a regression procedure that expresses the price of a product as a160function of the coefficients associated with each characteristic. The price of a new161product (or different product) can then be compared with that of the previously162existing product when one utilizes these coefficients…” They further highlight that163Court (1939) and Griliches (1961) allow for time dependence that does not require164any new methodology making it possible to simply use the previous time-independent165methodology restricting the regression to two consecutive periods. This will calculate166a measure of overall price change for the hedonic commodity.167168A second group of scholars pioneered by Colwell and Dilmore (1999) demonstrate169that Haas (1922a, 1922b) conducted a hedonic study more than fifteen years prior to170A. T. Court even though he never used the term ‘hedonic’. Haas analysed price per171acre adjusted for year of sale, road type, and city size, using data on 160 sales172transactions gathered from farm sales in Minnesota. The independent variables in the173hedonic analysis included depreciated cost of buildings per acre, land classification174index, soil productivity index, and distance to the city centre. Colwell and Dilmore175(1999) argue that Haas was influential but deny making a comprehensively strong176case for Haas as the pioneer to estimate a hedonic model. Surprisingly, their177alternative hypothesis is not Court (1939), but Wallace (1926), who used data178aggregated by county to calculate comparative farm land values in Iowa.179180181182 3183 Many other scholars contributed to the HPM over the years. Houthakker (1952) takes184into account the problem of quality variation within the theory of consumer185behaviour. He leaves out a multitude of corner solutions necessitated by conventional186demand theory and assumes that consumers purchase only a negligible fraction of all187goods available to them. This treatment is preserved by many subsequent authors to188maintain simplicity in the analysis. This early contribution of Houthakker was later189developed and extended by Becker (1965), Muth (1966), and Lancaster (1966) to190explicitly take into account the utility bearing characteristics in the context of191consumer behaviour.192193Griliches (1958) revived the HPM by further developing Court’s work. Griliches’s194paper embedded technological change and innovation into hedonic prices through195quality of goods. This hedonic model on demand for fertilizer contributed to196popularise the HPM at the early stage. Here demand for fertilizer relates prices and197mixes of different components of fertilizer (nitrogen, phosphoric acid and potash) to198derive better weights, which in turn are used to develop a series of constant quality199fertilizer quantities and prices. Griliches (1961) worked on automobile price indices200using automobile models as unit of analysis, and this work attracted considerable201attention although it was published in an ‘inaccessible’ publication (Goodman, 1998).202203Most important theoretical foundations of the HPM are Lancaster’s consumer theory204and Rosen’s model. These scholarly works are considered early but significant205contributions to the development of HPM. Lancaster (1966) establishes206microeconomic foundations for analyzing utility-bearing characteristics and applies207that to a range of topics including housing market, financial assets, the labour-leisure208trade off, and the demand for money. In his model quantities of goods and quantities209of characteristics are linked by a fixed relationship called “household production210function”. While households face a budget constraint defined over quantities of211goods, they derive utility from the quantities of characteristics these goods do212“produce”. With this model, Lancaster (1966) focuses on the demand side of the213market.214215Rosen (1974) integrates the HPM into standard economic theory. Inspired by work of216Houthakker (1952), Becker (1965), Muth (1966), and Lancaster (1966), he derives217“bid functions” of utility maximizing consumers and “offer functions” of profit218219220 4221 maximizing producers and shows that in equilibrium the hedonic price function222represents the joint envelope of these functions. In this form Rosen put forward a223meticulous explanation of the implicit market and hedonic prices in the context of224differentiated products. Using a vector of objectively measured characteristics225representing a class of differentiated products, he observes product prices and the226amounts of characteristics associated with each good to estimate a set of implicit or227hedonic prices. Because of the joint derivation of the hedonic price function from the228supply and the demand side, Rosen argued further that the entire set of implied prices229guides both consumer and producer locational decisions in characteristics space. His230study extends to analyse buyer and seller choices, market equilibrium and the231empirical implications of the HPM.232233Rosen’s theoretical foundation leads to a two step approach, which works as follows:234first, a hedonic equation is estimated. Subsequently, the implicit price of a235characteristic is derived as the partial derivative of the hedonic equation with respect236to that characteristic. Depending on the functional form involved, this derivative has237to be evaluated at a particular bundle of characteristics. In this context, the empirically238derived prices are embedded in a system of demand and supply equations.239240In Rosen’s model, income is directly incorporated in the budget constraints of the241consumer. This implies that the consumer’s marginal willingness to pay for a certain242implicit attribute may also change with his income. Buyers bid price (or willingness to243pay) for an attribute is a function of the utility level, the buyer’s income, and other244variables which influence tastes and preferences including education, age etc. An245inverse demand function can be estimated by using the marginal price as an246endogenous variable in the second-stage simultaneous equation. If it is possible to247trace back the inverse demand function based on the implicit marginal price function,248then measuring the utility change with respect to certain quality changes can also be249estimated by integrating the inverse demand.250251Lancaster’s and Rosen’s ideas differ from each other basically in two ways: the252functional form of hedonic regression and the answer to the question whether the253consumers buy a bundle of goods or separate goods. The fact that a bundle of goods254or separate goods are purchased have an impact on the implicit market as follows. The255Lancastrian index (1966) is based on the idea that usefulness of goods depends on256257258 5259 their characteristics, and goods can be arranged into groups based on their260characteristics. Consumers buy goods within groups based on the number of261characteristics they possess per dollar. According to Lancaster, the consumer’s utility262originates from the different characteristics (not just the quantities of the different263goods) which the goods themselves provide. Goods are members of a group and some264or all of the goods in this characteristic group are consumed in combinations, subject265to the consumer’s budget. Accordingly, the Lancastrian index is more appropriate for266consumer goods.267268Rosen’s model (1974), on the other hand, has two distinct steps: an initial step269involving an estimation of the marginal price for the attribute of interest (by270regressing the price of a commodity or good on its attributes), and a second step to271identify the inverse demand curve (or the marginal willingness to pay function) from272the implicit price function estimated in the first stage. Rosen maintains that there is a273range of goods, but that consumers typically do not acquire preferred attributes by274purchasing a combination of goods, rather each good is chosen from the spectrum of275brands and is consumed discretely. Accordingly, Rosen’s model looks appealing to276estimate demand for durable goods.277278Model specifications in these two theories differ as well. Lancaster’s consumer theory279assumes a linear relationship between the price of goods and the characteristics280contained in those goods. Implicit prices are therefore constant over their range of281characteristic amounts, and only a change in the combination of goods consumed is282possible. On the other hand, Rosen’s model assumes a nonlinear relationship between283the price of goods and their inherent attributes. The implicit price is not a constant,284but a function of the quantity of the attribute being bought and of the quantities of285other attributes associated with the good (depending on the actual functional form of286the equation).2872883. Estimation approaches, functional form and the model specification289290Regression analysis related estimation is the most popular estimation approach among291the scholars. Multiple regression analysis may either be an OLS regression or a292maximum likelihood estimation of the log-likelihood function derived from the293hedonic function. Both these estimation techniques try to find a vector of parameters294295296297 6298 that best matches the values of explanatory variables of observations with the299respective observed price. They differ by the criterion they use for identifying the best300match. The explanatory variables may be the characteristics values, or mathematical301transformations thereof, dummy variables or panel variables making it possible to302allow for non-linearity, variable interaction, or other complex valuation situations.303This information can be used to construct a price index that can be used to compare304the price of constant quality housing in different cities (spatial aspects) or in one city305over time (temporal aspects).306307Regression analysis related estimation approaches are common in HPM models in308real estate as well. The conventional hedonic price regression equation with regard to309the housing market is either rent or house value against the characteristics of the unit310that determine the respective rent or the value of the house. The fundamental311assumption of regression applies that the relevant determinants of the dependent312variable (rent, price, or value in this case) are known precisely and in advance. A313classical hedonic equation is as follows:314315 R = f (P, N, L, C, t )316317where R is rent or price of the house; P is property related attributes; N is318neighbourhood characteristics; L is locational variables; C is contract conditions and; t319is an indicator of time.320321In practice, various variables are applied based on the scholars’ preference or the322availability of data. Malpezzi (2003) notes that experience from many studies323suggests the following variables often appear in hedonic price analyses:324325 • Number of rooms and type of rooms (bedrooms, bathrooms, etc.)326 • Floor area327 • Category (Single family, multifamily, attached, detached, number of floors)328 • Availability and type of heating and cooling systems329 • Age330 • Structural features (presence of basement, fireplaces, garages, etc.)331 • Structural material used, and quality of finish332333334335336 7337 The functional form of the hedonic regression equation can either be linear, semi-log,338or log-log form. Most common is the semi-logarithmic form which has the advantage339that the coefficient estimates are proportions of the price that are directly attributable340to the respective characteristic. The advantage of the log-log form is that the hedonic341regression equation estimates elasticities with respect to each and every characteristic342under consideration. Taking logs of the dependent variable also takes into account343that prices are non-negative. This property is at odds with normality assumptions in344the case of a linear specification.345346Hedonic regression analysis is commonly used in real estate valuation as well. Sales347comparison approaches that are used in valuation of real estate are also variations of348hedonic-type measurements. One special case of the sales comparison approach, the349Sales Adjustment Grids takes into account a small number of recently sold properties350in the immediate vicinity of the subject property to value the attributes of that351property. An adjustment to the comparables is typically done using trend analysis,352matched-pairs analysis, or simple surveys of the market. Sales adjustment grids and353multiple regression models are theoretically the same. In comparison, multiple354regression analysis considers a large number of more geographically dispersed355property transactions to determine the significance and magnitude of the impact of356different attributes on property value. The multiple regression model builds on357statistical techniques, while the sales adjustment grid method constitutes a heuristic358approach.359360The HPM was later extended to take into account spatial dimension in the presence of361spatial dependence and spatial autocorrelation. One of the most popular and probably362the simplest ways of considering spatial aspects in the hedonic model is to include363distance from the central business district as an explanatory variable, although this364becomes nonsensical when there are multiple centres in a city. Anselin (1988) specify365two main spatial models in the literature namely the spatial lag model (LAG) and the366spatial error model (ERR). Recent spatial hedonic models of house prices reflect these367spatial econometric developments. The variations of more comprehensive hedonic368models with spatial variables include the lattice model, the geostatistical model and369the semiparametrics model. These hedonic models use spatial weights based on370alternative criteria that are defined based on the relationship between spatial units.371372373374 8375 4. A classification of real estate and housing studies that use the HPM376377The heterogeneous nature of houses, buildings and other real estate property justifies378the use of the HPM for estimating their demand or value. Houses, buildings and land379slots are different at least by their location, making it difficult to estimate the demand380for them generically. Therefore, the HPM takes into account the properties of real381estate separately and estimates prices based on the assumption that these properties382could be separated into characteristics such as attributes of the spatial unit (size of383plot, number of bed rooms, bathrooms, toilets, etc.), infrastructure and locational384attributes (including distance to the city centre), natural environment, social385environment, ecology, and quality of design and architecture.386387A caution is in order before proceeding to the empirical section. In the section on388estimation we stated that the conventional hedonic price regression equation with389regard to the housing market is either rent or house value against the characteristics of390the housing unit that determine the respective rent or the value of the house. There is391also a prolonged argument that rent values do not represent the actual value of real392estate. On the one hand, rent values may need adjustments for tax payments,393depreciation and other transactions costs etc. On the other hand, rents are based on394current demand and supply conditions rather than the actual value of underlying real395estate. However, since it is almost impossible practically to obtain the actual values of396real estate, most studies consider rent values to be proxies for value of the real estate397in empirical analyses.398399The initial classification based on the ISI Web of Knowledge citations shows that400there is a myriad of studies using the HPM to analyse various aspects of the housing401market and the impact of various factors on real estate prices in general and housing402prices in particular. We take into account the most cited 471 papers on housing and403real estate that use the HPM, and classify them into several sub-categories (see Graph4041). It turns out that majority of studies, i.e. 321 papers altogether, are empirical studies405looking at the real estate price dynamics. Out of those empirical scholarly works, 102406papers deal with real estate price dynamics in general while 219 papers examine price407dynamics related to a specific variable. A considerable amount of scholarly works, i.e.408134 papers in our total sample, are devoted to discuss theoretical and methodological409developments related to the HPM.410411412 9413 The sub-classification of empirical scholarly works show that most popular topic for414empirical studies is regarding the implicit price of neighbourhood characteristics and415their value addition to price or value of real estate. There were 178 papers on this416topic. Out of those papers, 134 of them studied value of a specific neighbourhood417characteristic and 44 papers examined value of neighbourhood characteristics in418combination. Even though there is an extensive discussion on implicit value of419structural characteristics of housing including energy efficient structures, surprisingly420we encountered only 16 studies that deal with this issue.421422In Graph 2, we further classify empirical work dealing with value of neighbourhood423amenities. Most common topic within this sub-category is estimation of the value of424environmental factors. Among these papers, 56 papers estimate the value of425environmental factors in general and 41 papers attempt to examine significance of air426pollution in particular. The sample of papers includes 33 papers on infrastructure427while other neighbourhood characteristics are researched frequently as well. Social428factors including racial segregation and crimes are under-researched among429neighbourhood characteristics.430431432433434 10435 The Hedonic436 Price Method437 in438 Real Estate439 and440 Housing441 Market442 Studies443 (471)444445446447448 Theoretical Empirical Historical Review449 and Studies Developments Articles450Methodological (321) (13) (3)451Developments (102- General452 (134) &453 219- Specific)454455456457458 Knowledge Depreciation Neighbourhood Income Price Property Wealth Wage459 Spillovers Index Characteristics Elasticity Elasticity Characteristics Redistribution (2)460 (2) (3) (178) (7) (7) (16) (4)461 (44- General462 &463 134- Specific)464465466467468 Graph 1. The types of real estate and housing market studies that use the hedonic price method469 Notes: Number of papers is shown in parentheses.470471472473474 11475 Neighbourhood476 Characteristics477 (134)478479480481 Social Environmental Infrastructure Other482 Factors Factors (33) (31)483 (14) (56)484485486487Racial Factors (11) Air Pollution (41) Service Centres (3) View (3)488489490491492 Crime (3) Forest Areas (5) Zoning (6) Open Space (15)493494495496497 Wetlands (7) Farm Lands (6) Proximity to City498 (13)499500501502503 Green Belts (3) Public Goods (13)504505506507508 Parks (3)509510511512513 Primary Schools (2)514515516517518 Graph 2. The types of neighbourhood characteristics and hedonic studies of housing market519 Notes: Number of papers is shown in parentheses.520521522523524 12525 5. Summary and future steps526527There are mainstream methodologies of hedonic regression, extensions to the existing528methodology, and innovations of new techniques. The HPM has evolved from a new529technology to the standard way that economists deal with heterogeneity of real estate and530housing.531532Most cited research articles evaluate environmental factors and air pollution as533determinants of real estate price dynamics, but only few studies cite to property534characteristics as a determinant of change of price. Our classification indicates that535neighbourhood characteristics are relatively over-researched as an explanatory variable of536real estate price or rent. Based on the initial classification, we also suggest that implicit537value of structural characteristics is under-researched. In general, implicit value of538environmental amenities in the neighbourhood and air pollution are relatively over-539researched. The effect of social factors, i.e. racial segregation and crimes, on real estate540value is under-researched.541542This analysis could be expanded in several ways. Literature related to the HPM is543enormous that explaining everything related to the methodology in one paper is virtually544impossible. One possible solution for this is to look at methodological developments and545empirical evidence as two separate review articles. An alternative way of dealing with546this vast literature is to consider papers in view of one specific characteristic or group of547characteristics at a time. This will provide an opportunity to critically discuss theoretical548applications and empirical investigations relevant to one class of papers that deal with549valuing one specific characteristic of houses. Other prospective work can emphasise on550hedonic literature that involve different asset types or different sub-markets. Those551different asset types and sub-markets may include single-family, multi-family, attached,552or detached housing markets, office markets or land markets. One last suggestion is to553either emphasis on hedonic literature related to temporal changes of house prices or554spatial changes at one time.555556557558559 13560 References561562Anselin, L. (1988), 'Spatial Econometrics: Methods and Models', Kluwer Academic563Publishers, Dordrecht, The Netherlands.564565Bartik, T. J. (1987), 'The estimation of demand parameters in hedonic price models',566Journal of Political Economy, Vol. 95, No. 11, pp. 81-88.567568Becker, G. S. (1965), ‘A theory of the allocation of time', The Economic Journal, Vol. 75,569No. 299, pp. 493- 517.570571Berry, B. J. L. and R. S. Bednarz, (1975), 'A hedonic model of prices and assessments for572single-family homes: does the assessor follow the market or the market follow the573assessor?', Land Economics, Vol. 51, No. 1, pp. 21-40.574575Colwell, P. F. and G. Dilmore, (1999), ‘Who was first? An examination of an early576hedonic study’, Land Economics, Vol. 75, No. 4, pp. 620-626.577578Court, A. T. 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