Real Estate Research Research Articles

SOME FEATURES OF CONDUCTING RESEARCH IN THE DIVISION OF RESIDENTIAL HOUSES OLD BUILDING

The solution of issues arising during the research on the division of residential buildings of old buildings is relevant for the theory and practice of conducting forensic examinations related to the development of options for the division of such real estate, given the uncertainty and contradictions in the data of certain documents (technical, legal) that are provided for research and changes in composition residential real estate in common ownership as a result of certain redevelopment actions, builder stvu, reconstruction.
 Errors (the results of incorrect calculations, inaccuracies, typos, etc.) allowed during the preparation of technical inventory documents, on the basis of which documents of title to real estate are drawn up, affect the calculations when developing options for the division of residential real estate. During long-term operation, all changes in the composition of households that are made by the co-owners of this property at different time periods, such as changes in the space-planning decisions of objects, engineering and technical equipment, landscaping, etc., must be reflected in the documents submitted for the study, including corrected ideal shares in connection with the improvements in residential real estate of the old buildings. These factors affect the completeness of the study and the formation of expert conclusions about the possible division of the old residential buildings.
 The analysis of normative and legal documentation was carried out, unified approaches to the research on the division of residential buildings of the old buildings were developed taking into account certain factors that affect the results of the conclusions of this type of expertise.

Property valuation methods in practice: evidence from Australia

PurposeImproving valuation accuracy, especially for sale and acquisition purposes, remains one of the key targets of the global real estate research agenda. Among other recommendations, it has been argued that the use of technology-based advanced valuation methods can help to narrow the gap between asset valuations and actual sale prices. The purpose of this paper is to investigate the property valuation methods being adopted by Australian valuers and the factors influencing their level of awareness and adoption of the methods.Design/methodology/approachAn online questionnaire survey was conducted to elicit information from valuers practising in Australia. They were asked to indicate their level of awareness and adoption of the different property valuation methods. Their response was analysed using frequency distribution, χ2 test and mean score ranking.FindingsThe results show that the traditional methods of valuation, namely, comparative, investment and residual, are the most adopted methods by the Australian valuers, while advanced valuation methods are seldom applied in practice. The results confirm that professional bodies, sector of practice and educational institutions are the three most important drivers of awareness and adoption of the advanced valuation methods.Practical implicationsThere is a need for all the property valuation stakeholders to synergise and transform the property valuation practice in a bid to promote the awareness and adoption of advanced valuation methods, (e.g. hedonic pricing model, artificial neural network, expert system, fuzzy logic system, etc.) among valuers. These are all technology-based methods to improve the efficiency in the prediction process, and the valuer still needs to input reliable transaction data into the systems.Originality/valueThis study provides a fresh and most recent insight into the current property valuation methods adopted in practice by valuers practising in Australia. It identifies that the advanced valuation methods could supplement the traditional valuation methods to achieve good practice standard for improving the professional valuation practice in Australia so that the valuation profession can meet the industry’s expectations.

Bibliometric analysis of hedonic price model using CiteSpace

PurposeHedonic price modeling (HPM) plays an important role in identifying factors affecting the price of goods or services. Many researchers have extensively used this modeling approach in their research work. However, there has been no bibliometric review that analyses the existing research, which makes use of HPM modeling. This study aims to provide a comprehensive visualized bibliometric and scientometric view of HPM usage in real estate research.Design/methodology/approachThe Scopus database was used to collect the bibliographic data of 269 publications from 1970 to 2019. CiteSpace software was then used to analyze and visualize the data.FindingsThe results revealed a significant increase in the annual number of HPM study publications. Core authors, participating countries and representative references have been identified. Research hotspots for the HPM studies were identified.Originality/valueWhile the study provides a methodological bibliometric review of HPM literature, the findings provide a very good platform for understanding the important elements of HPM usage in real estate research.

International real estate research

International real estate research

2016 Asia Pacific Real Estate Research Symposium Special Issue

2016 Asia Pacific Real Estate Research Symposium Special Issue

Editorial

Welcome to Volume 3, Issue 2 of the Journal of African Real Estate Research. This will be my final issue as Editor-in-Chief of JARER, and it has been a great pleasure to be a part of the journal’s recent relaunch. I am very confident of the journal’s continued success under the stewardship of my successor who will be announced in the new year. I am very grateful for the opportunity to play such an important role in the advancement of real estate research on the African continent.

A Strategy and Tool for Improving the Visibility of African Real Estate Research

The body of knowledge in the context of African Real Estate is growing constantly. The research discipline has attracted increased interest and thereby, the number of available publications has augmented. And while it becomes more and more difficult to keep track of the various research directions and contemporary research output in African Real Estate as the discipline progresses, new media provide viable solutions to the problem. This contribution outlines concepts for promoting research on African Real Estate, exemplified by the AfRER.org Research Database, and gives an outlook on scaling the list of available publications into a dynamic web-based system for supporting both authors and researchers on the quest for information and data. The contribution also makes suggestions on how to involve relevant stakeholders by fostering and developing research peer groups regarding African Real Estate research output. Entitled 'Promoting Publications in African Real Estate Research – Status Quo and Outlook‘, this topic was originally presented at the 2018 Annual Conference of the African Real Estate Society in Abeokuta, Nigeria.

News-based sentiment analysis in real estate: a machine learning approach

ABSTRACTThis paper examines the relationship between news-based sentiment, captured through a machine learning approach, and the US securitised and direct commercial real estate markets. Thus, we contribute to the literature on text-based sentiment analysis in real estate by creating and testing various sentiment measures by utilising trained support vector networks. Using a vector autoregressive framework, we find the constructed sentiment indicators to predict the total returns of both markets. The results show a leading relationship of our sentiment, even after controlling for macroeconomic factors and other established sentiment proxies. Furthermore, empirical evidence suggests a shorter response time of the indirect market in relation to the direct one. The findings make a valuable contribution to real estate research and industry participants, as we demonstrate the successful application of a sentiment-creation procedure that enables short and flexible aggregation periods. To the best of our knowledge, this is the first study to apply a machine learning approach to capture textual sentiment relevant to US real estate markets.

Using Online Search Queries in Real Estate Research with an Empirical Example of Arson Forecast

In this paper, I introduce a user's guide to Google Trends, a service created by Google to make statistics about online searches available to everyone at no cost. I review the service's advantages over conventional sources of data from a researcher's point of view. I also cover the most important stages of a real estate study that employs online search statistics from Google in a step-by-step user's guide. In the guide, I discuss how to compose and refine a list of search terms and how to access, download, process, and apply online search data in real estate research. I illustrate each step of an empirical real estate study. In the study, I test whether the intensity of online searches for specific keywords in metropolitan statistical areas (MSAs) can help to forecast future arson incidents in those areas. The findings reveal that lagged searches for “foreclosure” are significantly positively associated with the number of arson incidents in the same MSA where online searches have been conducted. The findings also show that lagged searches for “arson,” “restructuring,” and “strategic default” are negatively related to the number of intentional property fires.

Is there room for another hedonic model?

PurposeThis paper aims to explore the in-sample explanatory and out-of-sample forecasting accuracy of the generalized additive model for location, scale and shape (GAMLSS) model in contrast to the GAM method in Munich’s residential market.Design/methodology/approachThe paper explores the in-sample explanatory results via comparison of coefficients and a graphical analysis of non-linear effects. The out-of-sample forecasting accuracy focusses on 50 loops of three models excluding 10 per cent of the observations randomly. Afterwards, it obtains the predicted functional forms and predicts the remaining 10 per cent. The forecasting performance is measured via error variance, root mean squared error, mean absolute error and the mean percentage error.FindingsThe results show that the complexity of asking rents in Munich is more accurately captured by the GAMLSS approach than the GAM as shown by an outperformance in the in-sample explanatory accuracy. The results further show that the theoretical and empirical complexities do pay off in view of the increased out-of-sample forecasting power of the GAMLSS approach.Research limitations/implicationsThe computational requirements necessary to estimate GAMLSS models in terms of number of cores and RAM are high and might constitute one of the limiting factors for (institutional) researchers. Moreover, large and detailed knowledge on statistical inference and programming is necessary.Practical implicationsThe usage of the GAMLSS approach would lead policymakers to better understand the local factors affecting rents. Institutional researchers, instead, would clearly aim at calibrating the forecasting accuracy of the model to better forecast rents in investment strategies. Finally, future researchers are encouraged to exploit the large potential of the GAMLSS framework and its modelling flexibility.Originality/valueThe GAMLSS approach is widely recognised and used by international institutions such as the World Health Organisation, the International Monetary Fund and the European Commission. This is the first study to the best of the author’s knowledge to assess the properties of the GAMLSS approach in applied real estate research from a statistical asymptotic perspective by using a unique data basis with more than 38,000 observations.

Sentiments and Semantics: A Review of the Content Analysis Literature in the Era of Big Data

Drilling into data to understand property as a financial investment is one of the primary considerations of real estate research. This paper is designed to shed light on some of the newer and more innovative studies by highlighting the use qualitative data and content analysis throughout the literature to enhance our understanding of property. Particular attention is placed on linguistics research including readability metrics, semantic analysis, and sentiment analysis. Ocular tracking is also addressed as a way to evaluate unstructured forms of data. These methods are rigorous enough to be used on their own or as supplements to conventional pricing models, subject to the requirements of the research.

The Publication Bias of the Chinese Real Estate Research: A Meta-Analysis

This article applies a meta-analysis to investigate the results from hedonic housing model in China. This paper investigates the publication bias and authentic empirical effect among those researches. Partial correlation coefficients between building area and house price reveal a strong evidence of publication bias and a genuine positive effect, in which, the financial crisis in 2007 will increase the effects of building area on house price. And the linear model partially account for the publication bias.

Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network

Inaccuracies in property valuation is a global problem. This could be attributed to the adoption of valuation approaches, with the hedonic pricing model (HPM) being an example, that are inaccurate and unreliable. As evidenced in the literature, the HPM approach has gained wide acceptance among real estate researchers, despite its shortcomings. Therefore, the present study set out to evaluate the predictive accuracy of HPM in comparison with the artificial neural network (ANN) technique in property valuation. Residential property transaction data were collected from registered real estate firms domiciled in the Lagos metropolis, Nigeria, and were fitted into the ANN model and HPM. The results showed that the ANN technique outperformed the HPM approach, in terms of accuracy in predicting property values with mean absolute percentage error (MAPE) values of 15.94 and 38.23%, respectively. The findings demonstrate the efficacy of the ANN technique in property valuation, and if all the preconditions of property value modeling are met, the ANN technique is a reliable valuation approach that could be used by both real estate researchers and professionals.

Artificial neural network in property valuation: application framework and research trend

PurposeThe predictive accuracy and reliability of artificial intelligence models, such as the artificial neural network (ANN), has led to its application in property valuation studies. However, a large percentage of such previous studies have focused on the property markets in developed economies, and at the same time, effort has not been put into documenting its research trend in the real estate domain. The purpose of this paper is to critically review the studies that adopted ANN for property valuation in order to present an application guide for researchers and practitioners, and also establish the trend in this research area.Design/methodology/approachRelevant articles were retrieved from online databases and search engines and were systematically analyzed. First, the background, the construction and the strengths and weaknesses of the technique were highlighted. In addition, the trend in this research area was established in terms of the country of origin of the articles, the year of publication, the affiliations of the authors, the sample size of the data, the number of the variables used to develop the models, the training and testing ratio, the model architecture and the software used to develop the models.FindingsThe analysis of the retrieved articles shows that the first study that applied ANN in property valuation was published in 1991. Thereafter, the technique received more attention from 2000. While a quarter of the articles reviewed emanated from the USA, the rest were conducted in mostly developed countries. Most of the studies were conducted by universities scholars, while very few industry practitioners participated in the research works. Also, the predictive accuracy of the ANN technique was reported in most of the papers reviewed, but a few reported otherwise.Research limitations/implicationsThe articles that are not indexed in the search engines and databases searched and also not available in the public domain might not have been captured in this study.Practical implicationsThe findings of this study reveal a gap between the valuation practice in developed and developing property markets and also the contributions of real estate practitioners and universities scholars to real estate research. A paradigm shift in the valuation practice in developing nations could lead to achieving a sustainable international valuation practice.Originality/valueThis paper presents the trend in this research area that could be useful to real estate researchers and practitioners in different property markets around the world. The findings of this study could also encourage collaboration between industry professionals and researchers domiciled in both developed and developing countries.

Skyscrapers in Santiago: 300 meters of globalization agenda

PurposeThe purpose of this paper is to assess alternative economic explanations of buildings’ height in Latin America and Chile, inductively producing a theory about skyscrapers’ height in emerging countries. In the quest for height, global exposure as advertising guides developers located in emerging economies, while ego-building for investors.Design/methodology/approachThis paper uses mixed methods triangulation (MMT). Findings with small sample econometrics for 38 cities from 13 different countries are re-interpreted by linguistically analyzing 11 semi-structured interviews with local experts in Santiago.FindingsGlobalization is the main determinant of skyscrapers height in the Latin American region, its interaction with the need to portray management and technical skills of developer firms, determines a process toward over-construction.Research limitations/implicationsBecause of small sample bias, the quantitative results are not fully reliable, but this is precisely why it makes sense to use MMT.Practical implicationsSantiago offers a valuable case study because, on the one hand, Chile was the first Latin American country to undertake neoliberal type reforms, as early as 1973. On the other, the tallest Latin American skyscraper is to be completed in this city by 2015. The theory developed, derived from the evidence and the perceptions, has a Global South reach and can open-up an empirical research agenda.Originality/valueThis paper innovates in real estate research by using MMT, not just to confirm quantitative findings, but as an inductive theory building tool. It also analyses Latin America, a region with scarce presence in the literature.

Entropy – what kind of bet is real estate – really?

PurposeThe purpose of this paper is to describe the way real estate data collection and dissemination can be improved.Design/methodology/approachThe paper investigates that how information entropy fits into real estate research and data using Kelly betting as a framework.FindingsAvailability of real estate data is constrained by a host of impediments, paywalls and other archaic practices that limit the way it can be used to make worthwhile progress in the field.Practical implicationsReal-estate data will become available. Those who try to keep data close and restrict their common use will become irrelevant.Originality/valueEntropy indicates uncertainty and chaos. Using this highly developed and respected area of science as context one can see how real estate research suffers from its present mindset.

The Real Estate Academic Leadership (Real) Rankings for 2013–2017

The Real Estate Academic Leadership (REAL) rankings for authors and institutions began in 2015 with a straightforward approach to measure author and institutional contributions to real estate research. While real estate research may traditionally be allied with business schools, authorship varies across university programs, as well as public and private organizations. That level of diversity makes it difficult to easily rank programs or departments, resulting in the university or organization as being the primary unit of analysis. The initial rankings in 2011–2015 attempted to highlight the authors and institutions making the greatest contributions to the field of real estate research based on publications in the top three peer-reviewed real estate journals. To some extent, the 2012–2016 rankings served as a check on the rankings from 2011– 2015 to see if there were any major issues with the simple methodology used in the initial rankings. The 2013–2017 REAL rankings continue to highlight that part of the movement in the rankings can be attributed to individual movement to institutions.

The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model

This paper employ the discrete hidden Markov model (HMM) in order to capture information about the Markov switching model?s inner states that is not directly observable, and to pre-detect the real estate business cycle?s volatility trend. The empirical results show that this HMM can capture the asymmetry in the duration of states. Compared with the real estate leading indicator announced by the Taiwan Real Estate Research Center, this HMM yields the same results in terms of forecasting the trends of cycle fluctuations. The explanatory power of the HMM in 4-steps out-of-sample forecasting is supported both conceptually and methodologically.

Design beyond zoning: Negotiating architectural form and bulk restrictions at HL23

This article uses Neil M. Denari’s HL23 condominium tower in West Chelsea, New York City, as the lens through which to focus on the architectural design pathway in discretionary reviews to negotiate architectural form and bulk restrictions. The argument states that creative architectural forms can appropriate an unorthodox site configuration in accord with planning objectives and that review procedures are needed to recognize and evaluate such attempts. The objective of this article is, therefore, to elucidate the way in which design in discretionary reviews can be used as a method to negotiate the complex relationships between architectural form and zoning constraints within an existing system. As the shape of HL23 evolved in response to a complicated constellation of site conditions and zoning amendments, relationships between architectural design and urban planning process combine to frame case study research on the zoning background and authorization procedure that issued design beyond zoning. Because hardened setback regulations dramatically limited the profitability ratio of the project, design was used to negotiate the complex relationships between architectural form and bulk restrictions. When elucidating the design pathway pursued to realize the building, flexibilities within zoning are unlocked. This article has been developed accordingly: It examines HL23’s complicated site; it contextualizes the zoning amendments that reduced the project’s profitability ratio; it explicates the shape of bulk restrictions; it analyzes the digression categories; it synthesizes form and digression through an analytical study of the design pathway; it demonstrates that design in discretionary reviews can be used as a method to negotiate the complex relationships between architectural form and zoning constraints; and it discusses flexibilities in zoning. Data for this article were collected through literature research, direct observation, archival research, drawing analysis, and interviews. The findings have bearing on research and practice in architecture, urban design, urban planning, and real estate.

Real estate research in Europe

PurposeAgainst the background of initiatives, which have taken place to foster real estate research in Europe, this article seeks to analyse important dimensions of that research.Design/methodology/approachThe article investigates the evolution from 2000 to 2015 in the proportion of papers published by authors with a European affiliation in the three main international real estate journals (Real Estate Economics,Journal of Real Estate Finance and EconomicsandJournal of Real Estate Research). Then, focusing on papers with at least one European author and/or concentrating on Europe, the article analyses papers published from 2008 to 2015 in the two main European real estate journals (Journal of European Real Estate ResearchandJournal of Property Research) by authors’ country of affiliation, by country of study and by theme. Finally, we analyse links between author’s country of affiliation and country of study and theme, respectively.FindingsThe results show that the proportion of papers published by European authors in the three main international real estate journals has increased during the 2000-2015 period. The author's analyses of papers published in the two European real estate journals suggest that UK-based researchers are the most prolific. There is also a strong “home bias” in that authors largely focus on the country in which they are based. The interest in housing and valuation increased markedly during the period. Finally, the article reports linkages between country of affiliation and theme.Originality/valueThis paper should provide a much clearer understanding of several aspects of real estate research in Europe.