2026-09-20T14:50:41Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1221962021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrea Chegut.
Pérez Sarralde, Sebastian.
Massachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Development.
Massachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Development
Massachusetts Institute of Technology. Center for Real Estate
s-ck---
2019-09-17T16:28:53Z
2019-09-17T16:28:53Z
2019
2019
https://hdl.handle.net/1721.1/122196
1102320951
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M. in Real Estate Development, Massachusetts Institute of Technology, Program in Real Estate Development in conjunction with the Center for Real Estate, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-64).
This study introduces a geographical approach to analyze the market for new housing in Bogota, Colombia and address limitations of currently available research that is not sensitive to underlying spatial determinants in this city. The overall purpose of this investigation is to provide a framework to evaluate this market from a data-driven perspective in a context where information is often limited or disperse, while illustrating the importance of spatial interactions to develop estimations through quality-adjusted hedonic price models. The analysis is based on a dataset with information of more than 400 thousand new condominium transactions during the period between August 2010 and August 2018 in Bogota and surrounding municipalities. The properties are reverse-geocoded, assigned to their specific local planning jurisdictions within the city and surroundings, and analyzed in relation to their structural parameters.
The intersection between transactional and spatial data is explored to provide three approaches that contribute to the notion of the importance of social-political territorial subdivision as a driver of the residential market, while suggesting an initial route to develop accurate predictive models based on location rather than overly-detailed datasets for this city. The first approach consists of a comprehensive data summary that integrates several variables into graphical and geographical representations to portray urban characteristics of the city, reveal patterns and provide insights through the lens of the new housing market. The second approach involves the construction of quality-adjusted housing price indices for new housing.
The precision of a model with limited structural attributes is enhanced by including a combination of neighborhood fixed effects and factors that provide a qualitative assessment of the properties' socioeconomic context, a method that results effective to substantially augment coefficients of determination and lower residual standard errors. The scope of the price index is then expanded to analyze price dynamics according to locations and socioeconomic strata. Finally, the same methodology for the construction of the price indices is implemented to generate estimations for property area and prices at individual levels.
by Sebastian Perez Sarralde.
S.M. in Real Estate Development
S.M.inRealEstateDevelopment Massachusetts Institute of Technology, Program in Real Estate Development in conjunction with the Center for Real Estate
72 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Center for Real Estate. Program in Real Estate Development.
The power of context and location : a spatial approach to model the market for new housing in Bogota, Colombia
Thesis
application/pdf
2019-09-17T16:28:53Z
Publication
Master
RED
unknown
unknown
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<Abstract>The precision of a model with limited structural attributes is enhanced by including a combination of neighborhood fixed effects and factors that provide a qualitative assessment of the properties' socioeconomic context, a method that results effective to substantially augment coefficients of determination and lower residual standard errors. The scope of the price index is then expanded to analyze price dynamics according to locations and socioeconomic strata. Finally, the same methodology for the construction of the price indices is implemented to generate estimations for property area and prices at individual levels.</Abstract>
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