{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/372293"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/372293","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Essays on Real Estate Finance and Machine Learning","abstract":"This thesis presents three essays on the intersection of real estate finance and machine learning. Chapter One introduces my thesis. Chapter Two investigates if machine learning methods can deliver significant out-of-sample improvement in the return prediction of U.S. REITs. I find that return predictability is improved and REIT investors experience significant economic gains when using machine learning forecasts as compared to traditional OLS forecasts. I also discover that REITs are more predictable than stocks. Chapter Three takes a deep dive to understand why REITs differ from stocks in their predictability when using machine learning. I find that the fundamental drivers of predictability for REITs are very different from stocks, that REITs are robust to the size of the predictor set whereas stocks are not, and that the ability of factors to explain the return of stocks has declined over time but it has held constant for REITs. Chapter Four looks at how machine learning can be useful in real estate research when dealing with missing data. I demonstrate impressive gains in out- of-sample predictions once I absorb observations with missing values. More importantly, I find that researchers may draw the wrong conclusions if they solely rely on complete but smaller data sets, as marginal relationships between independent and dependent variables can switch signs once models are permitted to absorb the much larger set of observations that contain missing values. Chapter Five concludes and wraps up my thesis.","abstract_html":"This thesis presents three essays on the intersection of real estate finance and machine learning. Chapter One introduces my thesis. Chapter Two investigates if machine learning methods can deliver significant out-of-sample improvement in the return prediction of U.S. REITs. I find that return predictability is improved and REIT investors experience significant economic gains when using machine learning forecasts as compared to traditional OLS forecasts. I also discover that REITs are more predictable than stocks. Chapter Three takes a deep dive to understand why REITs differ from stocks in their predictability when using machine learning. I find that the fundamental drivers of predictability for REITs are very different from stocks, that REITs are robust to the size of the predictor set whereas stocks are not, and that the ability of factors to explain the return of stocks has declined over time but it has held constant for REITs. Chapter Four looks at how machine learning can be useful in real estate research when dealing with missing data. I demonstrate impressive gains in out- of-sample predictions once I absorb observations with missing values. More importantly, I find that researchers may draw the wrong conclusions if they solely rely on complete but smaller data sets, as marginal relationships between independent and dependent variables can switch signs once models are permitted to absorb the much larger set of observations that contain missing values. Chapter Five concludes and wraps up my thesis.","abstract_has_math":false,"creators":["Leow, Kah Shin"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Lindenthal, Thies"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-09","date_published":"2024-05-09","updated_at":"2026-07-22T22:23:56Z","subjects":["Machine Learning","Real Estate Investment Trusts","REITs"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/7ba376f1-5ac1-48f7-a40d-160afdacacb7/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.111163","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lindenthal, Thies"]},{"key":"dc:creator","label":"Author","values":["Leow, Kah Shin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-05-09"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/372293"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Real Estate Investment Trusts","REITs"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/7ba376f1-5ac1-48f7-a40d-160afdacacb7/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.111163"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/c5ed967f-ff95-4686-aa7a-01323e634055/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents three essays on the intersection of real estate finance and machine learning. Chapter One introduces my thesis. Chapter Two investigates if machine learning methods can deliver significant out-of-sample improvement in the return prediction of U.S. REITs. I find that return predictability is improved and REIT investors experience significant economic gains when using machine learning forecasts as compared to traditional OLS forecasts. I also discover that REITs are more predictable than stocks. Chapter Three takes a deep dive to understand why REITs differ from stocks in their predictability when using machine learning. I find that the fundamental drivers of predictability for REITs are very different from stocks, that REITs are robust to the size of the predictor set whereas stocks are not, and that the ability of factors to explain the return of stocks has declined over time but it has held constant for REITs. Chapter Four looks at how machine learning can be useful in real estate research when dealing with missing data. I demonstrate impressive gains in out- of-sample predictions once I absorb observations with missing values. More importantly, I find that researchers may draw the wrong conclusions if they solely rely on complete but smaller data sets, as marginal relationships between independent and dependent variables can switch signs once models are permitted to absorb the much larger set of observations that contain missing values. Chapter Five concludes and wraps up my thesis."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["d095dc013013cefa85d50435702041a9","87eda9de84448d1f82354d60eee3eb5f"]},{"key":"dc:title","label":"Title","values":["Essays on Real Estate Finance and Machine Learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lindenthal, Thies"],"dc:creator":["Leow, Kah Shin"],"dc:date.issued":["2024-05-09"],"dc:description.abstract":["This thesis presents three essays on the intersection of real estate finance and machine learning. Chapter One introduces my thesis. Chapter Two investigates if machine learning methods can deliver significant out-of-sample improvement in the return prediction of U.S. REITs. I find that return predictability is improved and REIT investors experience significant economic gains when using machine learning forecasts as compared to traditional OLS forecasts. I also discover that REITs are more predictable than stocks. Chapter Three takes a deep dive to understand why REITs differ from stocks in their predictability when using machine learning. I find that the fundamental drivers of predictability for REITs are very different from stocks, that REITs are robust to the size of the predictor set whereas stocks are not, and that the ability of factors to explain the return of stocks has declined over time but it has held constant for REITs. Chapter Four looks at how machine learning can be useful in real estate research when dealing with missing data. I demonstrate impressive gains in out- of-sample predictions once I absorb observations with missing values. More importantly, I find that researchers may draw the wrong conclusions if they solely rely on complete but smaller data sets, as marginal relationships between independent and dependent variables can switch signs once models are permitted to absorb the much larger set of observations that contain missing values. Chapter Five concludes and wraps up my thesis."],"dc:format.checksum.md5":["d095dc013013cefa85d50435702041a9","87eda9de84448d1f82354d60eee3eb5f"],"dc:identifier.doi":["https://doi.org/10.17863/CAM.111163"],"dc:identifier.uri":["https://www.repository.cam.ac.uk/bitstreams/c5ed967f-ff95-4686-aa7a-01323e634055/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/372293"],"dc:rights":["https://www.repository.cam.ac.uk/bitstreams/7ba376f1-5ac1-48f7-a40d-160afdacacb7/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"dc:subject":["Machine Learning","Real Estate Investment Trusts","REITs"],"dc:title":["Essays on Real Estate Finance and Machine Learning"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-22T22:23:56Z"}