Hedonic Models with Redevelopment Options under Uncertainty

In the hedonic model, implicit market prices can be interpreted as the present values of rents per unit of each hedonic characteristic. But when rents rise, there may be substantial value associated with the option to redevelop to higher intensity per unit land value. In the presence of option value, we first demonstrate that hedonic linear regressions should include an additive nonnegative term for the value of the option. This term increases in the variance of the underlying stochastic process. If this term is omitted, then estimates of implicit market prices for desirable (undesirable) characteristics will be biased downward (upward). This prediction is supported by recent empirical studies. We further suggest that future empirical work can employ the nonlinear functional form derived from our theory.     

Implicit Forward Rents as Predictors of Future Rents

This paper investigates the relation between the term structure of rents and future spot rents. A rich database of office rental agreements for various maturities is used to estimate the term structure of rents, and from this structure implicit forward rents are extracted. The data pertain to commercial properties in the three largest Swedish cities for the period 1998–2002. A positive relation between forward and spot rents is found in some regions, but forward rents underestimate future rent levels. Another contribution of the paper lies in the area of rental index construction. We provide evidence that rental indices should not only be quality constant ( i.e. , control for characteristics), but should also be maturity constant.     

Nonparametric Estimation of Dynamic Hedonic Price Models and the Construction of Residential Housing Price Indices

Parametric specifications for hedonic price equations are estimated using a data set from Alameda and San Francisco Counties and are compared to estimates using a nonparametric technique called locally weighted regression, LWR. LWR permits flexible estimation of the hedonic's curvature at median attributes and is less sensitive than standard regression techniques to the influence of unusual observations. The technique also avoids imposing a single functional form across time and municipalities. The LWR estimates of municipality-specific hedonics are then used to obtain implicit prices for housing attributes and to derive municipality-specific price indices. The results of extensive diagnostic checks of our technique are also reported.     

Housing Attributes Associated with Capital Gain

Housing units are heterogeneous goods. Rates of change in housing prices are typically modelled as if they arise from factors unrelated to the housing unit itself. For example, housing price increases in the latter part of the 1970s and early 1980s are argued to have arisen primarily from demographic factors and the differential effects of inflation on the effective rate of taxation on income from corporate capital and on owner-occupied housing. Cross-sectional variation in price inflation is not addressed. Consumers who purchased housing units are not indifferent to their attributes. To the extent that expectations vary within regional housing markets as a consequence of variation in housing attributes, standard linear hedonic price regression may generate biased estimates of implicit prices. This paper identifies sufficient conditions for the estimates of implicit prices in linear hedonic price regressions to be unbiased and generate unbiased estimates of implied price changes. Finally, this paper identifies living space (house size) as a significant attribute related positively to the increase in individual housing prices in a regional market.     

Explaining the Variability of Apartment Rents

The research reported here uses regression analysis to analyze rent variations in a sample of apartment data from the Phoenix metropolitan area. Many of the variables used in hedonic price studies of houses are found to be significant in explaining variations in apartment rent. There are differences between hedonic studies of houses and apartments particularly with respect to common area features or amenities. The analysis of various submarkets also produced interesting results. Various uses can be made of the results of this and similar studies by appraisers (market-derived adjustments), property managers (setting rents) and feasibility analysts (the design of apartment projects).     

Influence of Rent Differentials on the Choice between Office Rent Contracts with and without Relocation Provisions

In this paper we concern ourselves empirically with the influence of rent differentials on the choice between rent contracts with and without relocation provisions investigating the effects of relocation provisions on office rents. The study departs from the extant literature by using a switching simultaneous-equations model to estimate hedonic rent functions for alternative office lease contracts with choices of relocation provisions. The empirical results suggest that price considerations and certain lease terms are important determinants of the choice of office relocation rights. The results also suggest that certain tenant characteristics, like tenant size and creditworthiness, have only a marginal effect on the choice of relocation rights.     

Property Taxes and Residential Rents

Property taxes are a fundamental source of revenue for local governments, constituting 73% of local government tax revenue in the United States. In this article, we empirically investigate the impact of residential property taxes on residential rents. Using data from the American Housing Survey and the National League of Cities, we estimate numerous specifications of a hedonic rent equation with comprehensive unit-level, neighborhood-level and city-level controls. We find that a one standard deviation increase in the property tax rate raises residential rents by roughly $400 annually.     

Interpretable machine learning for real estate market analysis

Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle.     

Towards a differentiated modeling of origin effects in hedonic analysis: An application to auction prices of specialty coffee

Origin matters. This has been shown by numerous studies using either discrete choice or hedonic approaches to derive implicit prices for origin as a product attribute. In most of the hedonic studies, intercept dummies were introduced for specific regional origins and statistically significant coefficients of those variables were seen as an indication of either a superior or inferior reputation, compared to products from other origins. We argue that hedonic pricing models of this type may be too simple to detect the true origin effects if assessments of a product’s sensory quality are available and interact with prior beliefs about reputation. Based on a supply-and-demand framework to explain auction prices, a reduced-form hedonic pricing model is suggested that includes intercept- as well as slope-dummy effects of the regional origin. Because reputation and the objective product quality are particularly important for markets of differentiated, high-quality foods and beverages, we analyze electronic auction markets for specialty coffees. The findings for the Cup of Excellence data reveal that it is important to distinguish reputation, sensory quality, and their interaction as determinants of coffee auction prices, as well as varying origin impacts across market segments.     

On the Use of Spline Smoothing in Estimating Hedonic Housing Price Models: Empirical Evidence Using Hong Kong Data

Traditionally in estimating hedonic housing price functions, investigators use parametric models involving specific functional forms and a finite number of unknown parameters. Some investigators have questioned whether the underlying theory is capable of conveying sufficient information to enable a correct and successful specification of parametric models and have instead proposed the less restrictive semiparametric approach to the problem. In this paper, we illustrate how the technique of smoothing splines can be used to estimate hedonic housing price models. Smoothing splines are a powerful approach to the analysis of housing data as they are exceptionally flexible in their functional forms and provide a computationally tractable method even with a large number of explanatory variables. Our illustration takes the form of a rather limited, but very promising, application with Hong Kong data. In the forecasting comparison, the spline smoothing procedure outperforms the traditional parametric Box–Cox model in mean square error terms for out-of-sample predictions. Our results also suggest that the distortion caused by underfitting the model is smaller for spline smoothing than for the kernel and k-nearest-neighbor semiparametric procedures.     

A Semiparametric Method for Estimating Local House Price Indices

Spatial autoregressive hedonic models utilize house prices lagged in space and time to produce local house price indices, for example, the spatial and temporal autoregressive (STAR) model might be used this way. This paper complements these models with a semiparametric approach, the Local Regression Model (LRM). The greater flexibility of the LRM may allow it to identify space–time asymmetries missed by other models. The LRM is fitted to 49,511 sales from 1972Q1 to 1991Q2 in Fairfax County, Virginia. The local price indices display plausible and significant variations over space and time. The LRM price indices in selected neighborhoods are shown to differ significantly from those in some other neighborhoods. A new method for estimating standard errors addresses an overlooked problem common to all local price indices: how to evaluate the amount of noise in the estimates. Out-of-sample prediction errors demonstrate that LRM adds significant information to the hedonic model.     

The Workings of the London Office Market

This paper presents estimates of an equilibrium-based dynamic adjustment model of the office market, using supply and demand relationships to link construction, absorption, vacancies and rents to employment growth and real interest rates. The model is estimated using data from the City of London office market over 1977–1996. The model tracks the market dynamically, and the severe 1985–1996 cycle is shown to be related to the cycle in employment growth and the movement of real interest rates. The latter directly affects both construction and real rent levels.     

Is quality accreditation effective? Evidence from the childcare market

The ineffectiveness of a quality accreditation mechanism can be attributed to the inability of the accreditation status to provide consumers with information they do not already possess. I present a structural model of demand allowing consumers to infer quality from both accreditation status and firm reputation. I then estimate this model to assess the effectiveness and the impact of the national accreditation system for childcare centers on consumer welfare. My results suggest that disregarding the endogeneity of firms' accreditation choices significantly underestimates the effectiveness of the accreditation system. However, on average consumers do not gain much information beyond what they have inferred from a firm's reputation. The estimates of structural parameters are then used to quantify the value of this information to consumers.     

Do Tenants Capture the Benefits from the Low‐Income Housing Tax Credit Program?

This article compares the rent savings accrued by recipient households over the life cycle of Low‐Income Housing Tax Credit (LIHTC) projects to their allocated tax credits. A simple two‐stage empirical procedure is developed and implemented for a selected medium‐sized metropolitan statistical area. Using hedonic pricing parameters estimated in the first stage, LIHTC ceiling rents are compared to predicted market rents. The findings indicate rent savings constitute a relatively small fraction of the programs costs, suggesting developers and investors may capture some of the program's benefits. As this finding characterizes only one potential source of benefits of the LIHTC program, a brief discussion of other potential benefits to low‐income households supplements the analysis.     

Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach

Urban rail transit investments are expensive and irreversible. As people differ with respect to their demand for trips, their value of time, and the types of real estate they live in, such projects are likely to offer heterogeneous benefits to residents of a city. Defining the opening of a major new subway in Seoul as a treatment for apartments close to the new rail stations, we contrast hedonic estimates based on multivariate hedonic methods with a machine learning (ML) approach. This ML approach yields new estimates of these heterogeneous effects. While a majority of the “treated” apartment types appreciate in value, other types decline in value. We cross‐validate our estimates by studying what types of new housing units developers build in the treated areas close to the new train lines.     

How Much Will Consumers Pay? A Hedonic Analysis of the Cable Television Industry

Because cable television is the classic example of a bundled commodity, it is difficult to determine how consumers value individual cable networks offered on a typical system. This paper uses a modified hedonic framework to determine the marginal willingness to pay by consumers for individual cable networks. The traditional hedonic framework is adapted to allow for the lack of competition on the supply side of the market. It is clear that consumers do value some types of programming more than other types. Sports, news, and family programming all have positive implicit prices while program guides have negative marginal prices.     

The Spatial Proximity of Metropolitan Area Housing Submarkets

An important question related to housing submarket construction is whether geographic areas must be spatially adjacent in order to be considered the same submarket. Housing consumers do not necessarily limit their search to spatially concentrated areas and may search similarly priced neighborhoods located throughout a metropolitan area when making housing consumption decisions. This article examines two alternative procedures for delineating submarkets: one that combines adjacent census block groups into areas with enough transactions to estimate the parameters of a hedonic house price equation and a second that permits spatial discontinuities in submarkets. The criterion used to evaluate the alternative techniques is the accuracy of hedonic house price predictions.     

Repeat Sales as a Matching Estimator

The most common approaches for constructing house price indices—hedonic price functions and the repeat sales estimator—focus on changes over time in mean prices. Though the hedonic approach is less wasteful of data than the repeat sales estimator, it relies on an accurate specification of the underlying econometric model. I suggest using a matching estimator as an alternative to the hedonic and repeat sales approaches. Like the repeat sales approach, a matching estimator uses pairs of sales from different dates to estimate the mean difference in sales prices over time. The matching approach preserves much larger sample sizes than the repeat sales estimator while requiring less preimposed structure than the hedonic approach. The matching approach makes it easy to characterize changes in the full distribution of house prices.     

the evaluation of innovation projects

Abstract . Any realistic assessment of research projects must allow for possible failures. Scaling down the Potential gain from each project by its probability of achievement ignores the expenditure on unsuccessful projects and is therefore misleading. The method proposed here is more sound: it uses a concept termed the Survival Probability Function, which is simply the probability that a Project will still exist at various times in the future. This function can be derived from estimates for the survival factors and durations of project stages. The function can then be used to calculate the statistics of project successes and failures, and can be employed in a standard Risk Analysis. More simply, if can be used as a weighting function for cash flow patterns. This leads to a comparison method for projects with different risks and different potential returns, and thus to project portfolio selection.     

A Methodology for Constructing Vacant Land Price Indices

Existing estimates of movements in vacant land prices are limited to a few metropolitan areas and infrequent time intervals. This paper develops a new methodology for estimating vacant land price trends for subareas within states and metropolitan areas. It utilizes data from a sales ratio study, a large database available in most states. The methodology uses assessed value to control for "hedonic characteristics" associated with the property and its location. A model is developed to correct assessed value for measurement errors. Statistical results for forty-one Connecticut towns indicate that the model provides a reasonable compromise between data availability and accuracy of price trend estimates.