The capitalization of metro rail access in urban housing markets

Increasing access to public transportation (including metro rail) can help alleviate traffic congestion and address climate and environmental priorities. Living close to a metro line may be especially important in terms of providing improved commuting options. However, proximity to metro lines can also be associated with negative externalities, such as noise and crime, that may make living near a station less desirable. One way to assess the net value that residents place on metro rail access is to examine how proximity to metro lines is capitalized into house prices. Using a hedonic spatial difference-in-differences model, we analyze the impact of proximity to the stations on the Gold and Expo Lines in Los Angeles, California, on nearby house prices. Our findings suggest that the capitalization effect is heterogeneous. Some residents value living near new metro stations, while others do not. Overall, our results provide evidence that the value residents place on metro rail access varies based on their income levels and other demographics.     

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.     

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.     

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.     

Estimating Homeownership Costs: Owners' Estimates of Implicit Rents and the Relative Importance of Rental Equivalence in the Consumer Price Index

This paper discusses the relative importance of Rental Equivalence (REQ) in the U.S. Consumer Price Index. A hedonic model of rents is estimated, and the resulting parameters are then used to compare hedonic estimates of implicit rents to homeowner estimates like those actually used to estimate the REQ expenditure weight. There are several reasons why hedonic estimates might be below actual implicit rents, and these effects are controlled for in the model. Owner estimates of implicit rents are then shown to be comparable to such hedonic estimates. Results support the method used to estimate the expenditure weight.     

Testing machine learning systems in real estate

Uncertainty about the inner workings of machine learning (ML) models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system-testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.     

Hedonic Amenity Valuation and Housing Renovations

Hedonic and repeat sales estimators are commonly used to value such important urban amenities as schools, environmental quality and access to transit. Given that property data often omits information on quality differences between same‐aged homes as well as changes in structural attributes over time, researchers must assume that property renovations are uncorrelated with neighborhood amenities. We formally test if this assumption is valid by incorporating detailed data on renovations in Charlotte, North Carolina. We begin by testing how the inclusion of minor and major home improvements influences hedonic and repeat sales indices. Results find limited bias in hedonic indices and that renovated properties are no more likely to be sold than nonrenovated properties. Using the introduction of Charlotte's light rail‐transit system in 2000, we estimate a positive bias of between 1.6% and 19.9% on the capitalized benefits of access to light rail due to omitted information on renovations. Our results show that a number of common data cleaning techniques used to address missing information on structural improvements may worsen this bias.     

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.     

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.     

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 Value of Access to Rail Transit in a Congested City: Evidence from Housing Prices in Beijing

Using unique data sets of Beijing's congestion patterns and housing prices, I find that consumers are willing to pay significantly more for access to rail transit in more congested areas. Transit accessibility, however, offers little travel advantage outside of dense urban areas. The expansion of the metro network mitigates the costs of road congestion, creating both private and social benefits. Two policy initiatives aimed at reducing congestion are found to have achieved positive value effects. Further analysis reveals heterogeneous demand for accessibility, with wealthier residents and those owning fewer cars paying a higher premium for access to rail transit.     

Gentrification Trends in New Transit-Oriented Communities: Evidence from 14 Cities That Expanded and Built Rail Transit Systems

Over 25 billion dollars were spent between 1970 and 2000 in 14 major cities in the United States on the construction of new rail transit lines. This massive investment in rail transit construction and expansion allows me to study the consequences of local public goods improvements for communities nearby new stations. This article uses a 14-city census tract–level panel data set covering the years 1970 to 2000 to document significant heterogeneity in the effects of rail transit expansions across the 14 cities. Communities receiving increased access to new "Walk and Ride" stations experience greater gentrification than communities that are now close to new "Park and Ride" stations.     

Estimating Bargaining Effects in Hedonic Models: Evidence from the Housing Market

Bargaining is common in markets for heterogeneous goods and differences in bargaining power between buyer and seller affect the negotiated transaction price. Previous research has found systematic evidence in the housing markets that weak buyers pay higher prices and weak sellers receive lower prices for their homes. Earlier work has modeled the bargaining effect as a parallel shift in the hedonic function, implicitly assuming that attribute shadow prices were unaffected by the bargaining process. In this paper, we use a sample of home sales where the seller's bargaining power is weakened by the fact that the home is vacant at the time of sale to test whether the effect of bargaining is best captured by a shift in the hedonic constant or whether the attribute shadow prices vary as well. The question is significant for property valuation where estimation of the marginal value of an attribute is commonly used to adjust comparable sales data. We find strong confirmation that bargaining power influences the negotiated price. We also find evidence that bargaining power alters attribute prices, although we do not find a consistent pattern across markets.     

Virtual Word of Mouth and Willingness to Pay for Consumer Electronic Innovations

The authors examine the impact of virtual word‐of‐mouth (vWOM) communication on willingness to pay (WTP) for an innovation. A series of hypotheses are developed that link vWOM to the credibility of innovation information, perceived utilitarian value, and the perceived hedonic value of an innovation, which are in turn hypothesized to influence WTP. The authors test these hypotheses using data collected in Japan from 658 potential adopters of e‐readers and from 565 potential adopters of smartphones. Findings indicate that, in both samples, vWOM is positively correlated with the perceived credibility of innovation information, which in turn is positively correlated with both perceived utilitarian value and perceived hedonic value. WTP is also positively correlated with an innovation's perceived utilitarian and perceived hedonic value. In addition, the path between vWOM and perceived hedonic value is positive and significant in both samples. However, the path between vWOM and perceived utilitarian value is positive and significant in the smartphone sample, but not in the e‐reader sample. The empirical findings provide support for theoretical arguments that link WTP for complex consumer electronic products to consumer perceptions of utilitarian and hedonic value. The results also have important implications for the creation of vWOM strategies designed to reduce the price sensitivity of potential adopters.     

Applying Hedonic Pricing Models and Factorial Surveys at Parker Pen to Enhance New Product Success

Walking the path from new product concept to successful commercialization is a tightrope act. Product developers must carefully balance a variety of factors, including predictions of consumer price sensitivity as well as which combination of product attributes will be most valued by the intended market. A well-chosen mix of analytical tools can enhance a firm's chances of accurately predicting market demand. Chuck Tomkovick and Kathryn E. Dobie describe how the integration of two product attribute assessment techniques–hedonic pricing models and factorial surveys–allows product designers to more accurately gauge price sensitivity and market receptivity to new product designs. They also describe how these analytical tools were used to improve decision-making in product development at the Parker Pen Company, and they discuss the role these tools can play in facilitating the transition from concept to commercialization. Hedonic price analysis is an econometric method for determining the value purchasers place on attributes of existing products. In product development, factorial surveys are used to identify the value members of the target market place on new product concepts and prototypes. When used in combination with identified hedonic prices, the responses to a factorial survey allow product developers to predict consumer willingness-to-pay for various combinations of new product attributes. Following development of prototypes for two new product lines, product developers at the Parker Pen Company used hedonic pricing models and factorial surveys as a means for reducing demand uncertainty and for clarifying what consumers were willing to pay for various combinations of product attributes that were under consideration. The integration and use of these techniques involved a five-step process of target market identification, product attribute identification, hedonic price estimation, administering of the factorial survey, and determination of consumer willingness-to-pay. The results of these analyses allowed Parker Pen to better focus product development efforts on those design elements for which test market customers indicated both demand and willingness-to-pay. The Parker Pen Company found hedonic pricing and factorial surveys useful for predicting both the rate and the degree of change in consumers' marginal utility for specific product attributes. The usefulness of these techniques also extends beyond the early stages of new product conception. These techniques are helpful in the development and implementation of dynamic new product marketing mix strategies, including such elements as product design, pricing, channel selection, and promotion.     

Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing

This article is motivated by the limited ability of standard hedonic price equations to deal with spatial variation in house prices. Spatial patterns of house prices can be viewed as the sum of many causal factors: Access to the central business district is associated with a house price gradient; access to decentralized employment subcenters causes more localized changes in house prices; in addition, neighborhood amenities (and disamenities) can cause house prices to change rapidly over relatively short distances. Spatial prediction (e.g., for an automated valuation system) requires models that can deal with all of these sources of spatial variation. We propose to accommodate these factors using a standard hedonic framework but incoporating a semiparametric model with structure in the residuals modeled with a partially Bayesian approach. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. Our model allows prediction at sampled or unsampled locations as well as prediction interval estimates. The nonparametric part of our model allows sufficient flexibility to find substantial spatial variation in house values. The parameters of the kriging model provide further insights into spatial patterns. Out–of–sample mean squared error and related statistics validate the proposed methods and justify their use for spatial prediction of house values.     

Impacts of Hedonic and Utilitarian User Motives on the Innovativeness of User‐Developed Solutions

When individual consumers develop products for their own use, they in part expect to be rewarded by the use value of what they are creating (utilitarian user motives), and in part expect to be rewarded intrinsically by such things as the fun and learning experience derived from creating it (hedonic user motives). This paper shows a first‐of‐type study to understand the relationship between individual consumers' motives to innovate and the novelty and utility of the solutions they develop. The theoretical framework integrates self‐determination theory and goal‐setting theory. The major findings of this study are that utilitarian user motives positively affect the utility of user‐developed innovations. In contrast, hedonic user motives drive solution novelty; the more an innovator is “in it for fun,” the more novel the solution developed. However, hedonic user motives also have an inverted U‐shaped relationship with solution utility. When the dominant motive for developing an innovation is the joy of the creative process rather than use value, the utility of what is developed is negatively affected. These findings are of research interest, and can be of significant practical interest to producers hoping to benefit from user‐developed innovations. For the first time, it has been possible to show that the adjustment of hedonic rewards, for example by means such as gamification, can affect the nature and utility of solutions individuals create.     

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 Effect of Community Gardens on Neighboring Property Values

Cities across the United States that have considerable vacant land are debating whether to foster community gardens on that land, while cities with land shortages are debating when to replace gardens with other uses. Meanwhile, many cities are looking for new ways to finance green spaces. Little empirical evidence about the neighborhood impacts of community gardens is available, however, to inform the debate or to help cities design financing schemes. This article estimates the impact of community gardens on neighborhood property values, using rich data for New York City and a difference-in-difference specification of a hedonic regression model. We find that gardens have significant positive effects, especially in the poorest neighborhoods. Higher-quality gardens have the greatest positive impact.     

Advances in the competitiveness of pan‐European rail freight services: findings from a case study

The European rail freight market is ostensibly a free market where, from 1 January 2007, both incumbent and new‐entrant operators are able to compete on every line and in every European Union country. The main objective of this research paper is to assess the advances in the competitiveness of the pan‐European rail freight services operated by a new‐entrant (private) operator. Its main focus is to assess and contribute to the understanding of the advances towards competitiveness and the future prospects in the open European rail freight market, including dealing with challenges (e.g. dormant and departure of partners, suspension of the project, indistinct roles and responsibilities of operating partners) at different phases of the research, development and service offerings, that will be an important contribution to the Research and Development (R&D) policy and management arena in the Europe rail freight transport sector. The current research applies a case study research approach. The assessment of the rail freight service is performed by conducting two phases: first, a comparison of the progress between first and second year of the REorganisation of Transport networks by advanced RAil freight Concepts (RETRACK) rail freight service, operated by a new entrant and conducted on the corridor between two hubs – Cologne, Germany, and Györ, Hungary, and secondly a comparison of the opinions of customers on the RETRACK service and its competitors. From the comparative study between the first and second years, the study finds that the new‐entrant operator was able to offer an increased number of services by consolidating cargo from satellite connections at both ends of the operational corridor by adopting a pragmatic and flexible approach. The customer satisfaction survey suggests that the new‐entrant operator offered better service in terms of price, transit time, reliability and information flow/management compared to its competitors' services (offered by incumbent rail operators) on the corridor. However, their service was inferior to that of its competitors, in terms of frequency and availability of service. These less‐well performed service factors have improved gradually over time. The study suggests that intra‐rail competition has improved, but that inter‐modal completion is yet to be achieved. The ups and downs of the project provide important lessons for R&D management, academia and policy makers. The study suggests that a pan‐European rail freight service can be efficiently and effectively run by new‐entrant operators, and this will lead to more intra‐modal competition. However, they have yet to achieve competitiveness that will result in a shift of cargo from road to rail by offering an improved service that at least matches the major attributes of road freight service, e.g. price, transit time, door‐to‐door service and working in a collaborative way with other actors.