The Annual Meeting of the RSAI – The Israeli Branch, Tel-Aviv University, January 10, 2010 Development and estimation of a semi- compensatory residential.

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The Annual Meeting of the RSAI – The Israeli Branch, Tel-Aviv University, January 10, 2010 Development and estimation of a semi- compensatory residential choice model with a flexible error structure Sigal Kaplan, Shlomo Bekhor, Yoram Shiftan Faculty of Civil and Environmental Engineering, Technion

Motivation When faced with many alternatives, people apply a sequence of non- compensatory heuristics followed by a compensatory evaluation (Payne, 1976).

are mostly Multinomial logit necessitate exogenous choice set formation choice set formation independent of individual characteristics Motivation Semi-compensatory models: are based on Manski’s (1977) formula have 2 J -1 theoretical choice sets for J alternatives are estimated only for a few alternatives involve thresholds that are independent of individual characteristics do not account for correlation patterns and population heterogeneity Residential choice models:

Research objectives To develop a semi-compensatory model for residential choice To accommodate correlations across alternatives and random taste heterogeneity in the model

Model formulation Universal realm of alternatives Chosen alternative Chosen alternative Viable choice set Viable choice set Preference structure Preference structure Utility maximization Utility maximization Unmanageable choice set Unmanageable choice set No choice Overtly specified criteria thresholds Overtly specified criteria thresholds Conjunctive heuristic Conjunctive heuristic Abort? No Yes Choice set formation stage Choice stage

Model formulation Observed choice i Observed choice set S Proposed model: Nested logit or random coefficients logit Multidimensional mixed ordered-response model Observed combination of criteria thresholds that yield the choice set S

Model formulation MMOP-NL model :

Model formulation MMOP-RCL :

Empirical context Positive Demand for public transport Revitalization of city center Local economic growth Local employment generation Negative Demand for private cars Formation of seasonal communities Competition with low income groups in the rental market Regional impact of students:

Survey design Product: rental apartmentsPopulation: Technion’s studentsSurvey type: stated preferenceSurvey duration: 1 monthSurvey method: web-based Incentive: 23 prizes ($1000 ) Technion campus

Survey design Utility-based choice stage Rank three most preferred apartments from the choice set Utility-based choice stage Rank three most preferred apartments from the choice set Conjunctive choice set formation Criteria thresholds specification (e.g., price, rooms, noise level, parking) Conjunctive choice set formation Criteria thresholds specification (e.g., price, rooms, noise level, parking) Questionnaire socio-economic, price perceptions, travel attitudes and study preferences Questionnaire socio-economic, price perceptions, travel attitudes and study preferences Questionnaire socio-economic, price perceptions, travel attitudes and study preferences Questionnaire socio-economic, price perceptions, travel attitudes and study preferences Conjunctive choice set formation Criteria thresholds specification (e.g., price, rooms, noise level, parking) Conjunctive choice set formation Criteria thresholds specification (e.g., price, rooms, noise level, parking) Utility-based choice stage Rank three most preferred apartments from the choice set Utility-based choice stage Rank three most preferred apartments from the choice set Verification No Yes Respondent’s criteria thresholds and chosen apartment Synthetically generated apartment dataset 3 < j <100 Verification Yes SQL query Yes No Database Respondent’s information Respondent’s information

Survey design

Model specification Three criteria are represented in the estimated model: apartment sharing neighborhood monthly rent price Universal realm of alternatives: 200 apartments adjacent to campus with little employment or leisure far from campus with leisure activities, shopping and jobs Explanatory variables: personal characteristics apartment attributes Nested structure: floor number Taste variation: renovation status, view and security bars.

Model estimation results

Conclusions The model estimation results shows the importance of incorporating a flexible error structure into semi-compensatory models The proposed model is a viable option for real-world applications and it can be readily incorporated within activity-based models and joint residential and transportation models. The proposed semi-compensatory model: is applicable to large universal realms includes a probabilistic choice set formation dependent on individual characteristics includes a flexible error structure

Thank you! The Annual Meeting of the RSAI – The Israeli branch, Tel-Aviv University, January 10, 2010