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Real Estate, Volume 1, Issue 1 (June 2024) – 7 articles

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22 pages, 3375 KiB  
Article
Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short- and Long-Term Rental Strategies
by Sam Martin, Thomas Dimopoulos and Martha Katafygiotou
Real Estate 2024, 1(1), 136-157; https://doi.org/10.3390/realestate1010007 - 5 Jun 2024
Viewed by 1301
Abstract
Understanding the optimal strategy for a real-estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in touristic areas such as Paphos, Cyprus, where there is no clear distinction as to whether short- or [...] Read more.
Understanding the optimal strategy for a real-estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in touristic areas such as Paphos, Cyprus, where there is no clear distinction as to whether short- or long-term approaches are optimal. This study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data were collected from AirDNA and long-term data were manually collected from real-estate agents’ websites. Furthermore, Random Forest, K-Nearest Neighbour, and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between datasets, with the best-performing model for short-term properties being the Random Forest model (R-squared: 0.843), and the distance-based Multiple Linear Regression approach being the best for long-term properties (R-squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It was found that locational characteristics did not impact the returns significantly when assuming that the property was located within a touristic area. Full article
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Figure 1
<p>Random Forest overview.</p> Full article ">Figure 2
<p>Price heatmap for the long-term properties.</p> Full article ">Figure 3
<p>Price heatmap for the short-term properties.</p> Full article ">Figure 4
<p>Random Forest feature importance (long-term predictions).</p> Full article ">Figure 5
<p>Random Forest feature importance (short-term predictions).</p> Full article ">Figure 6
<p>KNN long-term MSE variation.</p> Full article ">Figure 7
<p>KNN short-term MSE variation.</p> Full article ">Figure 8
<p>Costs of following the incorrect rental strategy.</p> Full article ">Figure 9
<p>Long-term (Red) and short-term (Blue) HBU properties (Paphos).</p> Full article ">
56 pages, 1824 KiB  
Article
An Agent-Based Market Analysis of Urban Housing Balance in The Netherlands
by Erik Wiegel and Neil Yorke-Smith
Real Estate 2024, 1(1), 80-135; https://doi.org/10.3390/realestate1010006 - 28 Apr 2024
Viewed by 790
Abstract
The Dutch housing market comprises three sectors: social-rented, private-rented, and owner-occupied. The contemporary market is marked by a shortage of supply and a large subsidised social sector. Waiting lists for social housing are growing, whereas households with incomes above the limit do not [...] Read more.
The Dutch housing market comprises three sectors: social-rented, private-rented, and owner-occupied. The contemporary market is marked by a shortage of supply and a large subsidised social sector. Waiting lists for social housing are growing, whereas households with incomes above the limit do not or cannot leave the social sector. Government policy and market regulations change frequently, not least for political reasons. In view of commonly recognised problems in the housing market, this article considers the ‘internal demand’ of those households that are dissatisfied with their current residence. We examine the effects of regulatory policy by means of an exploratory agent-based simulation. The results provide perspectives on how internal demand is impacted by regulations in a housing market that is suffering from a shortage, and allow decision makers to weigh the pros and cons of policy measures. Full article
(This article belongs to the Special Issue Homeownership and Development)
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<p>High-level overview of component interaction.</p> Full article ">Figure 2
<p>Age of households (<b>A</b>) who are ‘homeless’, (<b>B</b>) in the social sector, or (<b>C</b>) living with parents.</p> Full article ">Figure 3
<p>Average waiting time for (<b>A</b>) all households, and (<b>B</b>) all successful renters.</p> Full article ">Figure 4
<p>After a relationship ended, the (<b>A</b>) average waiting time, and (<b>B</b>) number of households unable to find a home.</p> Full article ">Figure 5
<p>(<b>A</b>) average waiting time of households, and (<b>B</b>) number of households looking for a home.</p> Full article ">Figure 6
<p>(<b>A</b>) average age of households without a home or living with parents, and (<b>B</b>) average age of households socially renting.</p> Full article ">Figure 7
<p>(<b>A</b>) average waiting time of households, and (<b>B</b>) average age of households socially renting.</p> Full article ">Figure 8
<p>The (<b>A</b>) average private rent, and (<b>B</b>) average waiting time for renters successfully entering the social sector.</p> Full article ">Figure 9
<p>Number of households searching for a home.</p> Full article ">Figure 10
<p>Number of households searching for a home and average monthly rent paid in the private sector.</p> Full article ">Figure 11
<p>(<b>A</b>) Average monthly mortgage payment, and (<b>B</b>) average monthly rent paid in the private sector.</p> Full article ">Figure A1
<p>Lotteries for social housing.</p> Full article ">Figure A1 Cont.
<p>Lotteries for social housing.</p> Full article ">Figure A2
<p>Divorce and secondary waiting times.</p> Full article ">Figure A2 Cont.
<p>Divorce and secondary waiting times.</p> Full article ">Figure A3
<p>Maximum number of reactions.</p> Full article ">Figure A3 Cont.
<p>Maximum number of reactions.</p> Full article ">Figure A4
<p>Increased income limit social housing.</p> Full article ">Figure A4 Cont.
<p>Increased income limit social housing.</p> Full article ">Figure A5
<p>Waiting time for social housing.</p> Full article ">Figure A5 Cont.
<p>Waiting time for social housing.</p> Full article ">Figure A6
<p>Willing social market leavers.</p> Full article ">Figure A6 Cont.
<p>Willing social market leavers.</p> Full article ">Figure A7
<p>Migration.</p> Full article ">Figure A7 Cont.
<p>Migration.</p> Full article ">Figure A8
<p>Varying size compositions.</p> Full article ">Figure A8 Cont.
<p>Varying size compositions.</p> Full article ">Figure A9
<p>Varying market compositions.</p> Full article ">Figure A9 Cont.
<p>Varying market compositions.</p> Full article ">
15 pages, 1432 KiB  
Article
Impact of Green Features on Rental Value of Residential Properties: Evidence from South Africa
by Tawakalitu Bisola Odubiyi, Rotimi Boluwatife Abidoye, Clinton Ohis Aigbavboa, Wellington Didibhuku Thwala, Adeyemi Samuel Ademiloye and Olalekan Shamsideen Oshodi
Real Estate 2024, 1(1), 65-79; https://doi.org/10.3390/realestate1010005 - 20 Mar 2024
Viewed by 1090
Abstract
In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how [...] Read more.
In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and implement policies targeted at incentivising the inclusion of green features in existing and new residential properties in South Africa. Full article
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<p>Research framework.</p> Full article ">Figure 2
<p>Most frequent 20 words in the adverts for residential properties.</p> Full article ">Figure 3
<p>Word co-occurrence analysis of adverts for residential properties.</p> Full article ">Figure 4
<p>Sensitivity analysis of the random forest model.</p> Full article ">
24 pages, 334 KiB  
Article
Using Co-Ordinate Systems in Hedonic Housing Regressions
by Steven B. Caudill, Neela Manage and Franklin G. Mixon, Jr.
Real Estate 2024, 1(1), 41-64; https://doi.org/10.3390/realestate1010004 - 12 Mar 2024
Viewed by 648
Abstract
Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities [...] Read more.
Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities and dis-amenities. As an alternative to these, relatively recent research advocates a latitude–longitude co-ordinate system for incorporating distance information into hedonic house price regressions. This study shows that many of the claims made in this research, particularly those referencing the elimination or diminution of “biases of coefficients of non-distance variables”, are given the particulars of the Monte Carlo experiments, not possible to investigate. We further show, both analytically and with our simulations, that there is no omitted variable bias present in their simulations because their randomly generated non-distance variable is uncorrelated with any of the other variables used in their regression models. Full article
15 pages, 641 KiB  
Article
Real Estate Valuations with Small Dataset: A Novel Method Based on the Maximum Entropy Principle and Lagrange Multipliers
by Pierfrancesco De Paola
Real Estate 2024, 1(1), 26-40; https://doi.org/10.3390/realestate1010003 - 31 Jan 2024
Cited by 1 | Viewed by 1377
Abstract
Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, [...] Read more.
Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, underscore the importance of adopting advanced approaches to obtain accurate valuations, especially with small property samples. The objective of this study is to explore the applicability of the Maximum Entropy Principle to real estate valuations with the support of Lagrange multipliers, emphasizing how this methodology can significantly enhance valuation precision, particularly with a small real estate sample. The excellent results obtained suggest that the Maximum Entropy Principle with Lagrange multipliers can be successfully employed for real estate valuations. In the case study, the average prediction error for sales prices ranged from 5.12% to 6.91%, indicating a very high potential for its application in real estate valuations. Compared to other established methodologies, the Maximum Entropy Principle with Lagrange multipliers aims to be a valid alternative with superior advantages. Full article
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<p>Average real estate values for the residential segment of Naples, with the “Centre” area delimited by a blue line (source: <a href="http://www.immobiliare.it" target="_blank">www.immobiliare.it</a> (accessed on 20 December 2023) [<a href="#B40-realestate-01-00003" class="html-bibr">40</a>]).</p> Full article ">
22 pages, 2043 KiB  
Article
Housing Choices of Young Adults in Sweden
by Mats Wilhelmsson
Real Estate 2024, 1(1), 4-25; https://doi.org/10.3390/realestate1010002 - 12 Dec 2023
Cited by 1 | Viewed by 1288
Abstract
This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and [...] Read more.
This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and the regional disparities. The study uncovered intraregional variations that illustrate how different municipalities in Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors such as unemployment significantly impact this pattern. Housing market dynamics, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. These findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and social capital (such as social cohesion and inclusion). Full article
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<p>Methodological process.</p> Full article ">Figure 2
<p>Young adults living at home are distributed across municipalities and over time. The figure (<b>a</b>) represents the distribution between municipalities (all years). It is a so-called Violin plot [<a href="#B38-realestate-01-00002" class="html-bibr">38</a>] (Hintze and Nelson, 1998), where we, in the middle, have a box plot [<a href="#B39-realestate-01-00002" class="html-bibr">39</a>] (Tukey, 1977) with the median, 25th, and 75th percentiles, and outliers (dots). The contour around the box plot shows the cumulative distribution. The plot (<b>b</b>) shows the box plots per year from 1998 to 2021.</p> Full article ">Figure 3
<p>Spatial distribution of young adults living at home. The map shows Sweden and the spatial distribution of young adults living at home in 2020, which ranges from 14.6% to 75.8%. Municipalities with a dark red–orange colour show the highest ratios [45.2–75.8%], which are above the average in Sweden. Municipalities with an orange colour [37.75–45.2%] cover the average ratios in Sweden, while light orange [32.7–37.75%] and yellow municipalities [14.6–32.7%] are below average. The maps have been constructed using the Stata command grmap.</p> Full article ">Figure 4
<p>Scree plots of the eigenvalues after PCA for each category. The scree plot was introduced by Catell (1966) [<a href="#B45-realestate-01-00002" class="html-bibr">45</a>] and shows the eigenvalue for each component from the principal component analysis. Here, we use the cutoff value of 1 to decide the number of components to be used in the later spatial autoregressive panel data models. The first five show the eigenvalues sorted from the largest to the lowest from the principal component analysis from each category (Housing Market, Socioeconomics, Demographics, Culture and Location), and the last figure shows the eigenvalues from a principal component analysis of all the variables. We used the commands pca and screeplot in Stata.</p> Full article ">Figure 4 Cont.
<p>Scree plots of the eigenvalues after PCA for each category. The scree plot was introduced by Catell (1966) [<a href="#B45-realestate-01-00002" class="html-bibr">45</a>] and shows the eigenvalue for each component from the principal component analysis. Here, we use the cutoff value of 1 to decide the number of components to be used in the later spatial autoregressive panel data models. The first five show the eigenvalues sorted from the largest to the lowest from the principal component analysis from each category (Housing Market, Socioeconomics, Demographics, Culture and Location), and the last figure shows the eigenvalues from a principal component analysis of all the variables. We used the commands pca and screeplot in Stata.</p> Full article ">Figure 5
<p>Maps based on components from the principal component analysis. The maps show Sweden in 2020 and the spatial distribution of the four components used in the spatial autoregressive model, namely the components Rental housing (red), Low income and unemployment (blue), Long-term unemployment (green), and Voting participation (purple). All the variables are components of the principal component analysis, and the statistics range from minus to plus values, with an average of zero over the 1998–2021 study period. The maps have been constructed using the Stata command grmap.</p> Full article ">Figure 5 Cont.
<p>Maps based on components from the principal component analysis. The maps show Sweden in 2020 and the spatial distribution of the four components used in the spatial autoregressive model, namely the components Rental housing (red), Low income and unemployment (blue), Long-term unemployment (green), and Voting participation (purple). All the variables are components of the principal component analysis, and the statistics range from minus to plus values, with an average of zero over the 1998–2021 study period. The maps have been constructed using the Stata command grmap.</p> Full article ">
3 pages, 331 KiB  
Editorial
Real Estate: Discovering the Developments in the Real Estate Sector Using the Current Research Challenges
by Pierfrancesco De Paola
Real Estate 2024, 1(1), 1-3; https://doi.org/10.3390/realestate1010001 - 8 Oct 2023
Viewed by 1919
Abstract
“Agenda 2030” is a wide-reaching plan established by the United Nations, in which 17 Sustainable Development Goals (SDGs) with 232 related indicators highlight the most important economic, social, environmental and governance challenges of our time [...] Full article
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