Housing Choice in a Regulated Market: A Nested Multinomial Logit Analysis

The paper integrates several important elements of the institutionalized and regulated nature of the housing market and analyzes the relationship between household type and housing choice in Vienna within a nested multinomial logit framework. In particular, the concept of household specific choice sets is used to account for institutional, informational and income-based constraints of the choice process. Government subsidies (such as housing and rent allowances, non-interest-bearing state loans) are explicitly taken into account in the definition of variables. Housing choice is considered in three stages: the choice of a dwelling unit given dwelling type and residential zone, the choice of a dwelling type given residential zone, and the marginal choice of a residential zone. The coefficients are derived by means of a sequential ML-procedure. The empirical results clearly indicate that demographic variables have significant impacts on housing choice behavior. Income as a single explanatory variable as well as its interacting with size measures and the quality of the house are found to be important criteria for dwelling unit choice, as housing cost variables for dwelling type choice behavior. The dwelling unit and dwelling type choice submodels fit reasonably well, whereas the residential zone model is less successful.     

Mode Choice,Commuting Cost,and Urban Household Behavior*

Abstract. In this paper, we extend the partial equilibrium urban model of DeSalvo (1985) to include mode choice. DeSalvo demonstrated that the urban model of Muth (1969) was robust to the extension to leisure choice. We show that the model is robust to mode choice as well. In addition, we derive the comparative static results that commuters choose higher speed modes for longer commutes, at higher wage rates, with greater tastes for housing, and with lower housing prices. Also, for a given distance commuted, we derive the comparative static result that commuters chose shorter duration commutes at higher wage rates. Whereas it is typically assumed that marginal commuting cost is positive and non‐increasing with distance, we derive these results. Moreover, we derive the results that marginal commuting cost rises with an exogenous increase in housing price and falls with increased tastes for housing. We also explore the effects of exogenous commuting‐cost changes on the endogenous variables of the model. The remaining comparative static results on housing consumption and location are qualitatively the same as in DeSalvo.     

Microeconomic Formulation and Estimation of a Residential Location Choice Model: Implications for the Value of Time

Abstract We formulate a microeconomic model of residential location choice behavior as an aggregate of the individual behaviors of household members, subject to individual time constraints and a common income budget. A simplified version of the model is estimated from stated preference rank‐order data, yielding a function that may be interpretated as a conditional indirect utility function. We consider Box‐Tukey transformations, segmentation by income class, and a consistent treatment of data at different rank depths using the simultaneous mixed‐estimation method. Measures of the household's willingness‐to‐pay (through rents) for reducing travel times to work and study in the short run, are interpreted as subjective values of time and compared with such values derived from mode choice models. Our results are plausible, and consistent with recent findings showing that the short‐run benefits of transport projects derived by transport models are larger than benefits measured at the land use system.     

Modeling Strategies for Categorical Data: Examples from Housing and Tenure Choice

Models to investigate categorical data can be divided into preprocessing, limited parameterization, and formal logit models. To illustrate the advantages of preprocessing and limited parameterization models they are applied to a data set of tenure and type of housing choice before the data are examined with hierarchical logit and nested logit models. The preprocessing approaches are useful in selecting optimal subsets of independent variables with respect to the dependent variable. The ease of application and interpretation of a limited parameterization approach extends the clarity of the results from the preprocessing approaches. Because some variables are only relevant at specific levels of other independent variables, nonstandard (nested) logit models are necessary to understand the nested relationships.     

Calibration and Test of a Discrete Choice Model with Endogenous Choice Sets

Conventional discrete choice models assume implicitly that the choice set is independent of the decisionmaker's preferences conditional on the explanatory variables of the models. This assumption is implausible in many choice situations where the decisionmaker selects his or her choice set. This paper estimates and tests a discrete choice model with endogenous choice sets based on Horowitz' theoretical work. To calibrate the model, a new probability simulator is introduced and a sequential estimation procedure is developed. The model and calibration methods are tested in an empirical application as well as Monte Carlo simulations. The empirical results are used to test the theory of endogenous choice sets and to examine the differences between the new model and a conventional choice model in parameter estimates and predicted choice probabilities. The empirical results strongly suggest that ignoring the endogeneity of choice sets in choice modeling can have serious consequences in applications.     

A Selection Bias Approach to Destination Choice

Research on consumer search behavior commonly envisages destination choice as a two-step process: (1) delineate the search set, and (2) evaluate choices therein. However, much of the empirical work in destination choice—including logit and nested logit formulations—models only the latter, and not the set delineation itself. In the presence of correlation between error terms in set delineation and choice selection, statistical estimators are biased, a problem that Heckman and others have called selection bias. In this paper, an alternative two-stage method is proposed to estimate the parameters of models of set delineation and choice selection. Monte Carlo simulation is used to explore the properties of these two-stage estimators, and to show the magnitude of bias inherent in traditional methods of estimation.     

ON THE STABILITY OF HOUSING SECTOR INTERACTION: EVIDENCE FROM 42 METROPOLITAN AREAS*

ABSTRACT. Residential vacancy chain models based on White's (1971) application of Markov chain theory are used to simulate flows of housing vacancies through a set of interdependent housing market sectors. Measures of housing sector interaction are derived from such models. Our purpose here is to address the issue of time stationarity raised by their use. U.S. census data are available to calibrate such models at two, and sometimes three, points in time. Data from 42 different SMSAs are used to generate 56 cross-temporal comparisons with interobservation periods ranging from three to eight years. Stability of interaction is addressed from two perspectives–the sectoral perspective and the areawide perspective. Results are mixed and depend upon which measure of interaction is used, which perspective is addressed, and the duration of the interobservation period. However, within limits, findings support continued use of vacancy chain models.     

Markovian Models of Movement Within Urban Spatial Structures

Much work has assumed that movement within urban spatial structures is an “adaptive” process. Nonetheless, mathematical models have not yet been specified and tested which formulate both how different individuals “adapt” over time in destination or route selection, and how predictions about aggregate movement can be derived from postulates about different persons. Two adaptive first-order Markov models for heterogeneous individuals are suggested by the literature. When formulated and tested, however, these models are inadequate to describe travel within urban spatial structures. This implies that the use of Markovian processes to model movement may be overrated. More confidence may be placed in other formulations such as linear learning models of route and destination choice.     

School Choice and Educational Stratification

Recent growth in the number of school choice programs across the country has ignited debate on the stratifying effects of these programs. In the context of interdistrict open enrollment, this paper analyzes—both theoretically and empirically—how choice programs affect stratification levels through the mechanisms of (i) the relative characteristics of program participants and nonparticipants and (ii) the schooling choices of different groups of program participants. The theoretical analysis uses Monte Carlo simulation techniques to analyze a hypothetical world where interdistrict choice is available to students in three school districts that are allowed to vary in student composition, the type of students who take advantage of the interdistrict choice program, and schooling choices of students who open enroll. The results of these simulations provide an understanding of the conditions under which an interdistrict open enrollment program leads to increases, decreases, or no changes in stratification levels. The empirical analysis uses data from the universe of students attending Colorado public schools in 2009–10 to examine how the state's interdistrict choice program affects stratification levels. It also analyzes the factors responsible for any increases or decreases in stratification and finds both participation patterns and differences in schooling decisions across groups to play important roles. The paper concludes with a discussion of its implications for research and policy.     

A Micro-Econometric Store Choice Model Incorporating Multi- and Omni-Channel Shopping: The Case of Furniture Retailing in Germany

Online retailing and multi-/omni-channel shopping are gaining in importance. However, there is a significant lack of research focused on incorporating online shopping into models of spatial shopping behavior. The present study aims (1) to construct a store choice model which includes both physical and online stores as well as the opportunity for omni-channel shopping, and (2) to identify the main drivers of spatial shopping behavior given the availability of both channels. Based on a representative survey, this study employs a revealed-preference approach toward store choice and expenditures in furniture retailing. The statistical analysis is performed using a hurdle model approach, with the expenditures of individual consumers at (online or physical) furniture stores serving as the dependent variable. Results show that channel choice (online vs. offline) is mainly influenced by psychographic characteristics, place of residence, and age of the consumers. Store choice and expenditures are primarily explained by store features such as assortment size, omni-channel integration, and accessibility. This study demonstrates that e-shopping can be integrated into a store choice model and that both the modeling approach and the subsequent findings are of significance for retail companies and spatial planning.     

MODELING RESIDENTIAL CHOICE*

ABSTRACT. This paper presents a new approach in the modeling of residential choices for simulation purpose. The originality of the approach resides in the way recent developments in stochastic choice models are combined with deterministic mathematical programming which allows to define an equilibration process for simulation. In that respect, ex ante probabilities of housing choice based on random utility maximization under perceived constraints are distinguished from ex post probabilities of choice that respect supply constraints. The proposed model is tested on the Montreal Metropolitan Region, and a few simulation results based on a scenario of population aging and decrease in household size are analyzed.     

Measuring Impacts of Condition Variables in Rule‐Based Models of Space‐Time Choice Behavior: Method and Empirical Illustration

The use of rule‐based systems for modeling space‐time choice has gained increasing research interests over the last years. The potential advantage of the rule‐based approach is that it can handle interactions between a large set of predictors. Decision tree induction methods are available and have been explored for deriving rules from data. However, the complexity of the structures that are generated by such knowledge discovery methods hampers an interpretation of the rule‐set in behavioral terms with as a consequence that the models typically remain a black box. To solve this problem, this paper develops a method for measuring the size and direction of the impact of condition variables on the choice variable as predicted by the model. The paper illustrates the method based on location and transport‐mode choice models that are part of Albatross model—an activity‐based model of space‐time choice.     

STRUCTURAL ESTIMATION OF RESIDENTIAL MOBILITY AND HOUSING TENURE CHOICE*

ABSTRACT. This study is devoted to studying households'decisions to move and whether to rent or own after moving. It employs dynamic discrete choice models which condition households'decisions on their circumstances at every point in time during the length of the observation while accounting for individual heterogeneity. The results show that key dynamic elements, captured by means of lagged dependent variables, as well as household heterogeneity, are significant determinants of the tenure choice and mobility decisions. Moreover, homeowners are found to be responsive to housing market conditions when adjusting their housing stock. Housing-price appreciation is found to discourage renters from becoming homeowners. Finally, our results reject the proportional monetary transaction costs specification suggesting that monetary transaction costs might be fixed.     

School Choice in Milwaukee

For decades educational reformers have identified school choice programs as a strategy for restructuring public school systems. Practically every state has considered or adopted a school assignment program that qualifies as a "choice" initiative, one in which students and parents have some choice in school selection. Increasingly, school districts are contemplating plans that include a choice of private, as well as public, schools. One of the most far-reaching of these school choice plans is the Milwaukee (Wisconsin) Parental Choice Program, which, as legislated, allows parents to use vouchers to enroll their children in both sectarian and nonsectarian schools in the community. This paper explores the evolution of school choice in Milwaukee and examines the extent to which school choice is representative of other privatization efforts currently under way in the United States.     

Modeling Categorical Data with Chi Square Automatic Interaction Detection and Correspondence Analysis

For the past decade we have been concerned with the application of categorical data techniques to mobility and housing choice. In a previous issue of this journal we reviewed a number of these modeling techniques. We now extend that earlier work by introducing correspondence analysis as a geometric interpretative technique that fits in an intermediate position between CHAID and nested logit analysis. The method is illustrated on the same housing and tenure choice data as was used in the previous paper.     

RESIDENTIAL SORTING AND ENVIRONMENTAL EXTERNALITIES: THE CASE OF NONLINEARITIES AND STIGMA IN AVIATION NOISE VALUES

This paper explores the sorting process in response to differing levels of aviation noise exposure in a housing market. Spatiotemporal hedonic pricing (HP) and stated choice (SC) results reflect nonlinearities and stigma. The HP models reveal nonlinear noise depreciation increasing from 0.40 to 2.38 percent per decibel as noise increases, while the SC noise values are lower in an area with high long‐term noise exposure. These nonlinearities are attributed to the spatial sorting of noise tolerant individuals. HP results from the same “noisy” area show a “stigma” from noise during the first year after the complete removal of aviation noise.     

Simultaneous-Equation Systems Involving Binary Choice Variables

Simultaneous-equation systems of continuous variables are well known in the social sciences. Unfortunately, these methods cannot be used when dealing with discrete endogenous variables. Although for discrete types of data a broad range of methods and techniques have been developed, their applicability is restricted to single-equation systems (for example, logit or probit formulations) or to association-type models (log-linear models), mainly because of the lack of suitable estimation methods and computer programs. The basic problem is the multivariate error structure of the endogenous variables. This paper presents a simultaneous-equation system of binary endogenous variables. The model is applied empirically to longitudinal data on mode choice and is compared with the well-known log-linear model.     

Building Spatial Choice Models from Aggregate Data

Abstract Choice model construction is usually based on information about a number of separate choice situations, for which all relevant quantities are known. This paper concerns the case where only higher level, aggregate information is available about the choice results and the prevailing conditions. We demonstrate the applicability of a generic inverse parameter estimation method in estimating a model for grocery store choice. We also propose some enhancements to standard spatial choice models and demonstrate their applicability.