@misc{10481/73922,
year = {2022},
month = {2},
url = {http://hdl.handle.net/10481/73922},
abstract = {The worsening of housing problems in many countries has become a topic of global interest.
Researchers point to a variety of factors that influence individual housing tenure decisions. Our
study is based on longitudinal English Housing Survey (EHS) data (2008–2009 to 2019–2020, with
survey years matching financial years, i.e., running April–March) and identifies flows between
different forms of housing tenure in the U.K. and analyses conditional dependencies of a range
of EHS variables using a directed acyclic graph (DAG). More specifically, we take into account
variables such as first-time buyers (FTB), mortgage payments, rent payments, share of mortgage/rent
in household income, and receipt of housing benefit (HB), with some variables also reflecting a
regional breakdown (captured separately for London and England excluding London) to illustrate
the complex nature of regional differences in explaining changes in housing tenure. We address
some of the problems and challenges of the housing market in the U.K. today, and, in particular,
examine what influences private renters and those buying with a mortgage. A key conclusion from
this study is that housing benefit does not necessarily ease the way for private renters into their own
housing. The study is quantitative in nature and uses the English Housing Survey and Bayesian
network (BN) analysis. Unlike traditional methods, such as multiple regression or panel regression,
where the researcher somehow suggests the type of a relationship between certain variables, BN’s
learning algorithm analyses different iterations between variables and finds the most appropriate
relationships between them.},
publisher = {MDPI},
keywords = {Housing tenure in U.K.},
keywords = {Private renters},
keywords = {Buying with mortgage},
keywords = {Bayesian network (BN) analysis},
keywords = {Directional acyclic graph (DAG)},
title = {Private Renting vs. Mortgage Home Buying: Case of British Housing Market—A Bayesian Network and Directed Acyclic Graphs Approach},
doi = {10.3390/buildings12020189},
author = {Sobieraj, Janusz and Metelski, Dominik Igor},
}