Simplified probabilistic choice set formation models in a residential location choice context

Zolfaghari, Alireza; Sivakumar, Aruna; Polak, John

doi:10.1016/j.jocm.2013.12.004

Cited by 14 publications

(7 citation statements)

References 36 publications

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“…Both Kaplan et al (2013; and Zolfaghari et al (2013) examine the residential choice set formation problem. Kaplan et al develop a three-step procedure to develop the choice set.…”

Section: Literature Reviewmentioning

confidence: 99%

The evolution of choice set formation in dwelling and location with rising prices: A decadal panel analysis in the Greater Toronto Area

Hawkins

Habib²

2021

JTLU

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Home location choice is based on both the characteristics of the dwelling (e.g., size, style, number of bedrooms) and the location (e.g., proximity to work, quality of schools, accessibility). Recent years have seen a steep increase in the price of housing in many major cities. In this research, we examine how these price increases are affecting the types of dwelling and locations considered by households. A large sample of real estate listings from 2006 and 2016 from the Greater Toronto Area is used to develop the empirical models. Two recently developed discrete choice models are used in the study: a nested logit model with latent class feedback (LCF) and a semi-compensatory independent availability logit (SCIAL) model. A method of alternative aggregation is proposed to overcome the computational hurdle that often impedes the estimation of choice set models. We find a significant increase in the probability of larger households considering townhouses and apartments over detached single-family dwellings between 2006 and 2016.

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“…Both Kaplan et al (2013; and Zolfaghari et al (2013) examine the residential choice set formation problem. Kaplan et al develop a three-step procedure to develop the choice set.…”

Section: Literature Reviewmentioning

confidence: 99%

The evolution of choice set formation in dwelling and location with rising prices: A decadal panel analysis in the Greater Toronto Area

Hawkins

Habib²

2021

JTLU

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“…There are numerous possible techniques available to identify attributes ranging from qualitative in-depth interviews to almost theoretical approaches (Timmermans et al , 1982). In this study, the attributes and their levels were defined after consulting a local estate agent and conducting a literature review on housing preferences measured by discrete choice models (McFadden, 1978; Louviere and Timmermans, 1990a; Mulder, 1996; Rietveld and Wagtendonk, 2004; Zolfaghari et al , 2013) and hedonic price regression models (Rosen, 1974; Visser et al , 2008; Daniel et al , 2009; de Vor and de Groot, 2011; Francke and van de Minne, 2016). The final selection of attributes is displayed in Table I.…”

Section: Discrete Choice Experimentsmentioning

confidence: 99%

Homebuyers’ preferences concerning installed photovoltaic systems

Glumac¹,

Wissink

2018

JERER

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Purpose This paper aims to report on homebuyers’ preferences and willingness to pay for installed home photovoltaic systems. Their influence on the market position of a dwelling is relatively unknown. Considering that expected lifespan of photovoltaic systems is at least 25 years, it is likely that many dwellings with a photovoltaic system will enter the housing market. Design/methodology/approach Few houses with installed photovoltaic systems have been sold in the market to date. Lack of real market data imposes a method based on the stated preference data. Therefore, the general preferences toward photovoltaic systems are determined by a discrete choice model based on responses of 227 homebuyers in the Eindhoven region, The Netherlands. Further, the model estimates were used to assess the indirect willingness to pay for home photovoltaic systems. This initial willingness to pay is further reassessed with the direct willingness to pay collected in an open-ended questionnaire format. Findings Results of the model show that the homebuyers’ preferences for home photovoltaic systems are large and significant. In addition to general preferences, this article reports on the taste heterogeneity carried out by separating observations based on the respondents’ characteristics. For example, photovoltaic systems are more appealing to homebuyers in more urban or central neighbourhoods. Further, the results of the direct survey lead to the conclusion that people are probably willing to pay close to the replacement value of the system and only 22 per cent of all respondents did not want to pay anything for the installed photovoltaic system. Research limitations/implications These findings are exploratory and they raise a number of questions for further investigations, such as those regarding the real estate value of the installed photovoltaic systems. The reported findings must be regarded as local, thus further research is necessary to understand the impact on European housing markets. Practical implications Preferences and willingness to pay for home photovoltaic systems can provide a variety of economic, social and political recommendations to different interested parties such as homeowners, buyers, realtors, retailers, energy companies and governments. For instance, a homeowner would like to know what would be the effect of a photovoltaic system on the housing market. Originality/value As per the knowledge of authors, this is the first paper to estimate the impact of an installed photovoltaic system on housing choice, measured by stated choice data in the local housing market. It expands the existing body of knowledge for increasingly important issues of valuing and measuring preferences for photovoltaic systems installed on dwellings.

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“…Our example only features these qualities for simplicity of discussion. We note that in some contexts, deterministic choice sets are not uncommon: for example, when individuals are making residential location choices, some housing options may be deterministically excluded because the rents violate the individuals' income constraints Zolfaghari et al, 2013;Bhat, 2015). Moreover, decision trees that probabilistically predict an individual's choice set can be estimated.…”

Section: What Are Decision Trees and How Do We Use Them?mentioning

confidence: 99%

“…Before moving on to Subsection 3.2, we pause to briefly summarize why we believe the link between disjunctions-of-conjunctions and decision trees is important. First, as noted above, conjunctive rules and disjunctive rules are seen as important information processing strategies, and they have been applied in many choice modeling efforts (Foerster, 1979;Swait, 2001b;Gilbride and Allenby, 2004;Elrod et al, 2004;Martínez et al, 2009;Hauser et al, 2010;Hess et al, 2012;Zolfaghari et al, 2013;Truong et al, 2015). Being a generalization of these two rules, disjunctions-of-conjunctions may also be an important decision making strategy, but it has seldom been tested in choice modeling contexts.…”

Section: Non-compensatory Decision Rulesmentioning

confidence: 99%

“…In addition to making use of the observed thresholds, Kaplan et al model the choice of threshold, thereby allowing the model to be used for prediction with observations for whom thresholds have not been elicited. Another model that is estimated according to this approach is the model of Zolfaghari et al (2013). Though similar to the Kaplan et al models, Zolfaghari et al allow for the possibility that individuals do not make use of all elicited attribute thresholds.…”

Section: Choice-set Generation Via Non-compensatory Protocolsmentioning

confidence: 99%

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Machine Learning Meets Microeconomics: The Case of Decision Trees and Discrete Choice

Brathwaite¹,

Vij²,

Walker³

2017

Preprint

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In the 1960's, the logistic regression model from statistics and the binary probit model from psychology were linked with random utility theory, thereby connecting such methods with economic theory. Since then, the fields of statistics, computer science, and machine learning have created numerous methods for modeling discrete choices. However, these newer methods have not been derived from or linked with economic theories of human decision making. We believe this lack of economic interpretation is one reason discrete choice modelers have been slow to adopt these newer methods.Our paper begins bridging this gap by providing a microeconomic framework for decision trees: a popular machine learning method. Specifically, we show how decision trees represent a non-compensatory decision protocol known as disjunctions-of-conjunctions and how this protocol generalizes many of the noncompensatory rules used in the discrete choice literature so far. Additionally, we show how existing decision tree variants address many economic concerns that choice modelers might have. Beyond theoretical interpretations, we contribute to the existing literature of two-stage, semi-compensatory modeling and to the existing decision tree literature. In particular, we formulate the first bayesian model tree, thereby allowing for uncertainty in the estimated non-compensatory rules as well as for context-dependent preference heterogeneity in one's second-stage choice model. Using an application of bicycle mode choice in the San Francisco Bay Area, we estimate our bayesian model tree, and we find that it is over 1,000 times more likely to be closer to the true data-generating process than a multinomial logit model (MNL). Qualitatively, our bayesian model tree automatically finds the effect of bicycle infrastructure investment to be moderated by travel distance, socio-demographics and topography, and our model identifies diminishing returns from bicycle lane investments. These qualitative differences lead the bayesian model trees to produce forecasts that directly align with the observed bicycle mode shares in regions with abundant bicycle infrastructure such as Davis, CA and the Netherlands. In comparison, the forecasts of the MNL model are overly optimistic.

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Simplified probabilistic choice set formation models in a residential location choice context

Cited by 14 publications

References 36 publications

The evolution of choice set formation in dwelling and location with rising prices: A decadal panel analysis in the Greater Toronto Area

The evolution of choice set formation in dwelling and location with rising prices: A decadal panel analysis in the Greater Toronto Area

Homebuyers’ preferences concerning installed photovoltaic systems

Machine Learning Meets Microeconomics: The Case of Decision Trees and Discrete Choice

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