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Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation

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A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 25122

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Prof. Dr. Mirosław Bełej

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Guest Editor

Department of Spatial Analysis and Real Estate Market, University of Warmia and Mazury in Olsztyn, 15 Prawocheńskiego St., 10-720 Olsztyn, Poland
Interests: real estate economics; socio-economic geography; land management; spatial econometrics; housing market analysis; property valuation; cadastral systems, catastrophe theory

Prof. Dr. Małgorzata Krajewska

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Department of Geodesy, Spatial Management and Real Estate, UTP University of Science and Technology, Bydgoszcz, Al. prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland
Interests: real estate economics; property valuation; market analysis; socio-economic geography; land management; spatial planning; urbanization

Dr. Izabela Rącka

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Guest Editor

Department of Public Management and Law, Calisia University – Kalisz, Poland, ul. Nowy Świat 4, 62-800 Kalisz, Poland
Interests: real estate economics; property valuation; housing; market analysis; urbanization; urban renewal
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human economic and social activity takes place in a multi-dimensional space which can be viewed in terms of geographic, economic, natural, social, cultural, and planning dimensions. Rational management in a market economy requires making decisions in the conditions of several spatial conflicts. Strong competition in spatial solutions results most often from the convergence of preferences of individual entities, both public and private, as to specific places in space. The land is the most direct realization of this abstract concept of space, the result of which is a phenomenon of strong competition between different types of land use. The consequence of these processes is the need for administration, management, spatial planning, land tenure, and land valuation. This Special Issue focuses on urban and rural real estate development perspectives in terms of social, economic, and environmental impact. The multidimensionality of real estate creates an opportunity to present interdisciplinary research, as the area of real estate market research is a multifaceted platform for many scientific disciplines. We encourage authors to submit manuscripts on theoretical and practical solutions to spatial conflicts, taking into account the specificity of real estate. The previously mentioned topics are recommended, but the Special Issue is not limited to them. We believe the results of studies on the impact of the Covid-19 pandemic on property prices and land use, as well as gender discrimination and inequality in the acquisition of property rights could be expected.

Prof. Dr. Mirosław Bełej
Prof. Dr. Małgorzata Krajewska
Dr. Izabela Rącka
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Prof. Dr. Małgorzata Krajewska
Prof. Dr. Mirosław Bełej
Dr. Izabela Rącka
Guest Editors

Manuscript Submission Information

Keywords

land management
land use and spatial planning
land administration systems
land tenure
land valuation and mass appraisal
integrated rural development
sustainable urban development
suburbanization
landscape ecology
real estate market
spatial heterogeneity
geographic information system and GIS analysis

Published Papers (8 papers)

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Research

16 pages, 1440 KiB

Open AccessFeature PaperArticle

Factors Affecting Spatial Autocorrelation in Residential Property Prices

by Daniel Lo, Kwong Wing Chau, Siu Kei Wong, Michael McCord and Martin Haran

Land 2022, 11(6), 931; https://0-doi-org.brum.beds.ac.uk/10.3390/land11060931 - 17 Jun 2022

Cited by 4 | Viewed by 2252

Abstract

Within housing literature, the presence of spatial autocorrelation (S.A.) in housing prices is typically examined horizontally in a two-dimensional setting. However, in the context of apartment buildings, there is also a vertical component of S.A. for housing units located on different floor levels. This paper therefore explores the determinants of both horizontal and vertical S.A. within residential property prices. First, we posit that S.A. in housing prices is a consequence of the price discovery process of real estate, in which property traders acquire price information from recent market transactions (i.e., comparables) to value a subject property. Furthermore, we contend that the extent to which property traders rely on comparables to determine housing prices is governed by the liquidity and volatility conditions of the market, which in turn affects the magnitude of the S.A. By developing and testing several spatial autoregressive hedonic models using open market transaction data for the Hong Kong residential property market, we find that market liquidity tends to increase both vertical and horizontal S.A., whilst market volatility is more prone to increase vertical S.A. but depress horizontal S.A. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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28 pages, 2252 KiB

Open AccessArticle

Performance Evaluation of Land Administration System (LAS) of Nairobi Metropolitan Area, Kenya

by Daniel Orongo Nyangweso and Mátyás Gede

Land 2022, 11(2), 203; https://0-doi-org.brum.beds.ac.uk/10.3390/land11020203 - 28 Jan 2022

Cited by 3 | Viewed by 3245

Abstract

This paper aims to evaluate the internal processes of the current land administration in Kenya based on the following parameters that include ownerships, transactions, transfers, inquiries, public records of maps as attributes, issues, and customer satisfaction using stakeholder surveys and focused group discussions. A framework tool was developed for evaluation and shared with potential respondents who were either clients or staff working at the Ministry of Lands to obtain an overview of the performance of the documentation and registration processes of the land administration system (LAS). Data collected were processed and analysed using SPSS 26. To ascertain data reliability, the Cronbach’s alpha test was performed, and a coefficient of 0.908 was calculated, which indicated the presence of high internal consistency of the questions and relevance of the study subjects for the participants. The findings revealed the presence of emerging issues where an average of approximately 28% of clients do not have an idea of land registration transactions. In addition, in Kenya, similarly to other national mapping agencies in the developing world, pre-independence laws have begun, which need to be upscaled or revised to sustain and effectively address issues noted on land administration and policy. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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31 pages, 894 KiB

Open AccessArticle

Buying vs. Renting a Home in View of Young Adults in Poland

by Marek Bryx, Janusz Sobieraj, Dominik Metelski and Izabela Rudzka

Land 2021, 10(11), 1183; https://0-doi-org.brum.beds.ac.uk/10.3390/land10111183 - 03 Nov 2021

Cited by 10 | Viewed by 4258

Abstract

The deteriorating housing situation of young adults in many countries has become a subject of global interest. Researchers point to a number of factors that influence young adults’ decisions to own or rent a home. This paper examines the relationship between young adults’ inclination to own their own home and a range of different socio-economic factors. The study is of a quantitative-qualitative nature and was based on the results of a computer-assisted web interview (CAWI) questionnaire. The survey was conducted among young Poles aged 18–45 (n = 983). To analyse the results of the questionnaires, a logistic regression was used in which 24 different dichotomous and categorical variables were considered. Taking into account a number of individual characteristics (e.g., gender, education, hometown population size, etc.), the results show, for example, that single individuals and those with a higher level of tolerance for mortgage interest rates are more likely to own a home than to rent. This result highlights the desirability of easier access to mortgage credit. At the other end of the spectrum are those living with parents and those with dependents. The results imply that they are less interested in buying a home. Interestingly, declarations of a lack of funds for an own contribution when buying a new house/dwelling, or reluctance to expose oneself to financial difficulties throughout one’s life, do not discourage young adults from striving for their own dwelling, which further demonstrates the need to create appropriate mechanisms/instruments to facilitate the purchase of a dwelling for young adults. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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15 pages, 1177 KiB

Open AccessArticle

Nonlinear Influence of Public Services on Urban Housing Prices: A Case Study of China

by Lei Gan, Hong Ren, Weimin Xiang, Kun Wu and Weiguang Cai

Land 2021, 10(10), 1007; https://0-doi-org.brum.beds.ac.uk/10.3390/land10101007 - 26 Sep 2021

Cited by 9 | Viewed by 1861

Abstract

Owing to China’s rapid urbanization and internal migration, public services are unevenly distributed in cities, affecting urban housing prices. This study examines the dynamic effect of China’s public service levels on urban housing prices. We used the entropy method to calculate the public service index of 30 cities in China and a panel threshold regression model to explore the relationship between urban public service levels and housing prices. We found that the degree of the effect of public service levels on urban housing prices varies with the per capita disposable income of urban residents, demonstrating an inverted U-shaped curve. The role of public services in promoting urban housing prices increases with the increase in the level of urbanization. When the level of urbanization exceeds its threshold, the enhancement effect increases. These results help us better understand the theories of housing price changes in Chinese cities and support policymakers in formulating effective control measures for the housing market. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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29 pages, 10537 KiB

Open AccessArticle

Change Characteristics and Multilevel Influencing Factors of Real Estate Inventory—Case Studies from 35 Key Cities in China

by Sidong Zhao, Weiwei Li, Kaixu Zhao and Ping Zhang

Land 2021, 10(9), 928; https://0-doi-org.brum.beds.ac.uk/10.3390/land10090928 - 03 Sep 2021

Cited by 20 | Viewed by 3175

Abstract

High inventory is a common issue in urban real estate markets in many countries, posing a threat to the sustainable development of macroeconomics and society. This study built an analytical framework for the evolution of real estate inventory and its driving mechanisms and conducted an empirical study on 35 key cities in China. The findings show that, first, China has a huge real estate inventory with significant spatial heterogeneity. Second, the real estate inventory in China first rises and then falls, presenting an inverted U-shaped change trend; however, the spatial heterogeneity first falls and then rises, characterized by a U-shaped evolutionary change. Third, the present characteristics and evolutionary paths vary among different types of real estate inventory, mainly showing growth, stability, and inverted U-shaped changes. Fourth, the influencing factors of real estate inventory are increasingly diversified, and different factor pairs show bifactor-enhanced and nonlinearly-enhanced interaction effects, with a more intricate and complex driving mechanism. Fifth, four types of policy areas were divided according to the Boston Consulting Group Matrix, and it is recommended that the design of de-stocking policies should be dominated by “key factors” for cities in the stars and cows policy areas, while “important factors” and “auxiliary factors” should be equally emphasized for cities in the question policy area; the cities in the dogs policy area should keep the status quo as much as possible with avoidance of undesirable or excessive interventions. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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14 pages, 1253 KiB

Open AccessCommunication

Territorial Extrapolation of Basic Data as a Solution of the Problem of Its Deficiency during Mass Appraisal

by Jana Volkova, Elena Bykowa, Maria Hełdak, Katarzyna Przybyła and Sebastian Pawlak

Land 2021, 10(7), 750; https://0-doi-org.brum.beds.ac.uk/10.3390/land10070750 - 17 Jul 2021

Cited by 1 | Viewed by 2079

Abstract

The article is devoted to the application of the territorial extrapolation of basic data method during a mass (cadastral) assessment of a territory that is characterized by an acute lack of market information. In the framework of the study, an acute lack is understood as the conditions when for the assessing territory there are less than five transaction (offer) prices suitable for regression models. The idea of the method is to use market information of territories that are comparable in a composition of pricing factors and the nature of their influence on the cost, as well as in terms of price levels. The developed method includes such stages as collection of basic data, creation of thematic maps, grouping of estimated territories by price level and composition of pricing factors and modeling. The method was applied to assess land plots that have the type of permitted use “for individual housing construction” and belong to the mass appraisal segment “gardening and horticulture, low-rise residential buildings” in the settlements of the Republic of Udmurtia. The results of approbation shown that the method of territorial extrapolation helps to overcome an acute shortage of market information and build statistically significant models of the cadastral values of land plots. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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16 pages, 4352 KiB

Open AccessArticle

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

by Hamidreza Rabiei-Dastjerdi and Gavin McArdle

Land 2021, 10(6), 566; https://0-doi-org.brum.beds.ac.uk/10.3390/land10060566 - 28 May 2021

Cited by 7 | Viewed by 3330

Abstract

The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neighborhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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14 pages, 2349 KiB

Open AccessArticle

Property Mass Valuation on Small Markets

by Sebastian Gnat

Land 2021, 10(4), 388; https://0-doi-org.brum.beds.ac.uk/10.3390/land10040388 - 08 Apr 2021

Cited by 14 | Viewed by 2502

Abstract

The main bases for land taxation are its area or value. In many countries, especially in Eastern Europe, reforms of property taxation, including land taxation, are being carried out or planned, introducing property value as a tax base. Practice and research in this area indicate that such a change in the tax system leads to large changes in land use and reallocation. The taxation of land value requires construction of mass valuation system. Different methodological solutions can serve this purpose. However, mass land valuation requires a large amount of information on property transactions. Such data are not available in every case. The main objective of the paper is to evaluate the possibility of applying selected algorithms of machine learning and a multiple regression model in property mass valuation on small, underdeveloped markets, where a scarce number of transactions takes place or those transactions demonstrate little volatility in terms of real property attributes. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, considering the size of training datasets. Three types of models were employed in the study: a multiple regression model, k nearest neighbor regression algorithm and XGBoost regression algorithm. Training sets were drawn from a larger dataset 1000 times in order to draw conclusions for averaged results. Thanks to the application of KNN and XGBoost algorithms, it was possible to obtain models much more resistant to a low number of observations, a substantial number of explanatory variables in relation to the number of observations, a low property attributes variability in the training datasets as well as collinearity of explanatory variables. This study showed that algorithms designed for large datasets can provide accurate results in the presence of a limited amount of data. This is a significant observation given that small or underdeveloped real estate markets are not uncommon. Full article

(This article belongs to the Special Issue Property in the Space: Real Estate Spatial Analysis, Land Use, Urban-Rural Interactions, Management and Valuation)

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