Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)

The first chapter of this thesis examines the formation process of residential prices in Spain (1995Q1 – 2012Q4). We propose two models to compare their performance in the context of comparative dynamics and predictive capacity. A structural model is derived from an eclectic theoretical framework in...

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Detalles Bibliográficos
Autor: Rodríguez Ramírez, Ramiro J.
Tipo de recurso: tesis doctoral
Fecha de publicación:2017
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/22477
Acceso en línea:https://hdl.handle.net/20.500.14352/22477
Access Level:acceso abierto
Palabra clave:347.235(043.2)
Real property
Propiedad inmobiliaria
Econometría (Economía)
5302 Econometría
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spelling Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)Análisis de mercados inmobiliarios (un enfoque desde cointegración y econometría espacial)Rodríguez Ramírez, Ramiro J.347.235(043.2)Real propertyPropiedad inmobiliariaPropiedad inmobiliariaEconometría (Economía)5302 EconometríaThe first chapter of this thesis examines the formation process of residential prices in Spain (1995Q1 – 2012Q4). We propose two models to compare their performance in the context of comparative dynamics and predictive capacity. A structural model is derived from an eclectic theoretical framework in which we review published literature on the housing market and select a set of variables representative of this literature. We used GDP pre-capita, interest rates, the supply of new residential buildings and the gross residential-capital formation as explanatory variables for the average house price per square meter in Spain. The other model is generated by an algorithm known as GASIC2. Using our review of the literature we select a set of 46 variables, we form the respective database and let the algorithm to select the best model out the 2 (70 trillion) nested models. The condition imposed on the algorithm is to be parsimonious, i.e. having only 4 regressors. Annual theoretical effort of families to pay for their residence, the apparent concrete consumption, the mortgage interest rate and the real GDP are selected by GASIC to explain the average residential price in Spain; a similar model to the structural one. Our analytical framework is cointegration. Therefore, we assessed the integration order of both models’ variables. We identified all variables have order of integration of first degree (some with a structural break in the recent economic crisis). This leads us to test the hypothesis of cointegration. Proving such an existence, two error correction models (ECM) were estimated (one for the structural approach and one for the algorithmic) to calculate price and income elasticities, and produce dynamic forecasts. The long-term equations in both models behave similarly and give a good idea of the long-term equilibrium relationship between housing prices and their fundamentals. It is in the short term specification where the structural model and the algorithmic model differ. The model generated with GASIC has got a non-significant error correction mechanism, implying that the gap between the change in housing prices and longterm path is not traced. The consequence of such failure generates less accurate house price forecasts. However, the analysis of elasticities remains valid in both long and short term price equations...Universidad Complutense de MadridSosvilla Rivero, SimónUniversidad Complutense de Madrid20172017-08-0820172017-08-08doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisapplication/pdfhttps://hdl.handle.net/20.500.14352/22477reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/224772026-06-02T12:44:21Z
dc.title.none.fl_str_mv Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
Análisis de mercados inmobiliarios (un enfoque desde cointegración y econometría espacial)
title Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
spellingShingle Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
Rodríguez Ramírez, Ramiro J.
347.235(043.2)
Real property
Propiedad inmobiliaria
Propiedad inmobiliaria
Econometría (Economía)
5302 Econometría
title_short Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
title_full Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
title_fullStr Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
title_full_unstemmed Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
title_sort Quantitative analysis of commercial and residential real estate markets (an approach from cointegration and spatial econometrics)
dc.creator.none.fl_str_mv Rodríguez Ramírez, Ramiro J.
author Rodríguez Ramírez, Ramiro J.
author_facet Rodríguez Ramírez, Ramiro J.
author_role author
dc.contributor.none.fl_str_mv Sosvilla Rivero, Simón
Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 347.235(043.2)
Real property
Propiedad inmobiliaria
Propiedad inmobiliaria
Econometría (Economía)
5302 Econometría
topic 347.235(043.2)
Real property
Propiedad inmobiliaria
Propiedad inmobiliaria
Econometría (Economía)
5302 Econometría
description The first chapter of this thesis examines the formation process of residential prices in Spain (1995Q1 – 2012Q4). We propose two models to compare their performance in the context of comparative dynamics and predictive capacity. A structural model is derived from an eclectic theoretical framework in which we review published literature on the housing market and select a set of variables representative of this literature. We used GDP pre-capita, interest rates, the supply of new residential buildings and the gross residential-capital formation as explanatory variables for the average house price per square meter in Spain. The other model is generated by an algorithm known as GASIC2. Using our review of the literature we select a set of 46 variables, we form the respective database and let the algorithm to select the best model out the 2 (70 trillion) nested models. The condition imposed on the algorithm is to be parsimonious, i.e. having only 4 regressors. Annual theoretical effort of families to pay for their residence, the apparent concrete consumption, the mortgage interest rate and the real GDP are selected by GASIC to explain the average residential price in Spain; a similar model to the structural one. Our analytical framework is cointegration. Therefore, we assessed the integration order of both models’ variables. We identified all variables have order of integration of first degree (some with a structural break in the recent economic crisis). This leads us to test the hypothesis of cointegration. Proving such an existence, two error correction models (ECM) were estimated (one for the structural approach and one for the algorithmic) to calculate price and income elasticities, and produce dynamic forecasts. The long-term equations in both models behave similarly and give a good idea of the long-term equilibrium relationship between housing prices and their fundamentals. It is in the short term specification where the structural model and the algorithmic model differ. The model generated with GASIC has got a non-significant error correction mechanism, implying that the gap between the change in housing prices and longterm path is not traced. The consequence of such failure generates less accurate house price forecasts. However, the analysis of elasticities remains valid in both long and short term price equations...
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-08-08
2017
2017-08-08
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/22477
url https://hdl.handle.net/20.500.14352/22477
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Complutense de Madrid
publisher.none.fl_str_mv Universidad Complutense de Madrid
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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