Real estate market cyclical dynamics

2014 ◽  
Vol 10 (2) ◽  
pp. 241-262 ◽  
Author(s):  
Kim Hin/David Ho ◽  
Kwame Addae-Dapaah
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Real Estate Market ◽  
Var Model ◽  
Kuala Lumpur ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Price Response ◽  
Real Estate Cycle

Purpose – The purpose of this paper is to help us understand the real estate cycle and offers an analysis using a vector auto regression (VAR) model. The authors study the key international cities of Hong Kong, Kuala Lumpur and Singapore. The authors find four key outcomes. One, the real estate cycle is generally different from the underlying business cycle in local markets for the cities studies. Two, the real estate cycle is more exaggerated in the construction and development areas than in rents and vacancies. Three, the vacancy cycle tends to lead the rental cycle. And four, new construction completions tend to peak when vacancy is also peaking. The authors believe that future research should try to help understand the linkages that drive these outcomes. For example, are rigidities in the local permit and construction markets responsible for the link between construction peaks and vacancy peaks? Design/methodology/approach – Real estate market cyclical dynamics and its estimation via VAR model offers an insightful set of practical and empirical models. It affirms a comprehensive theoretical underpinning for analysing the prime office and residential sectors of the capitol cities of Kuala Lumpur, Singapore and Hong Kong in the fast developing Asia region. Its unrestricted form also provides an effective and insightful way of modelling real estate market cyclical dynamics utilising only real estate market indicators, furnished by real estate market data providers. Findings – The office rental VAR model for Singapore (SOR), KL (KOR) and HK (HOR) show good fits. In the HOR model, rents and vacancies are negatively signed and significant for certain lagged relationships with other variables and with rents themselves. The office CV VAR model for Singapore (SOCV), KL (KOCV) and HK (HOCV) show good fits. In the HOCV model, capital values (CVs) and initial yields are negatively signed and significant for certain lagged relationships with other variables and with CVs themselves. Impulse response functions specified for seven years to mirror a medium-term real estate market cycle “die out” to zero for the stationary VAR models that are estimated for the endogenous variables. The accumulated responses asymptote to some non-zero constant. Practical implications – The VAR model offers a complete and meaningful dynamic system of solely real estate variables for international real estate investors and policy makers in decision making. Its unrestricted form offers an effective and insightful way of modelling real estate market cyclical dynamics utilising only real estate market indicators, which can be reliably provided by a dedicated real estate information and consultancy provider of international standing. Originality/value – The theoretical model offers a complete dynamic model system of the real estate space market, comprising a unique system of six linked equations that denote the relationship among supply, demand, construction, vacancy and rent over time, inclusive of price response slopes and lags. The VAR model enables the investigation of the effect of the lagged values of all the variables concerned. It also enables the explicit and rigorous quantitative forecasts of say rents and CVs when the rest of the variable can be forecasted beforehand.

Godrej properties: valuation using the capital cash flow technique

2020 ◽  
Vol 10 (3) ◽  
pp. 1-23
Author(s):  
Rajni Kant Rajhans
Keyword(s):  
Real Estate ◽  
Cash Flow ◽  
Structural Changes ◽  
Real Estate Market ◽  
Firm Valuation ◽  
Free Cash Flow ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

Learning outcomes The case is focused to meet the following learning objectives: the readers will be able to recall basic cash flow estimation concepts; and the readers will be able to explain various features of capital cash flow (CCF). The participants will be able to implement the CCF model in real estate firm valuation. The participants will be able to compare CCF and free cash flow to the firm (FCFF) models. The participants will be able to evaluate the benefits of CCF over FCFF. The readers will be able to construct the CCF valuation model for firm valuation. Case overview/synopsis On 19th April 2019, Mr Kai, an analyst tracking real estate firms was excited to present to his team a new robust technique of firm valuation suitable for real estate companies, namely, the CCF technique and was also keen to deliberate on its application. Though the investment scope using this technique could be located in Godrej properties (GP), a reputed brand and the largest listed real estate developer by sales in 2018, yet, he was concerned about the assumptions of growth of real estate industry in India, in general, and the GP in particular. Importantly, this was because the real estate market in India was undergoing many structural changes. For instance, the buyers’ preferences were changing and unsold inventory in the industry was at its peak. Under these market conditions, an announcement was made by GP about a target return on equity of 20% in 2018–2023 expecting a dominant place in the real estate market in India, which also carried the threat of jeopardizing the reputation of GP, if under any circumstance the target was not accomplished. Complexity academic level Masters program. Supplementary materials Teaching notes are available for educators only. Subject code CSS: 11 Strategy.

scholarly journals Impacts of digitization on real estate sector jobs

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daniel Piazolo ◽  
Utku Cem Dogan
Keyword(s):  
Real Estate ◽  
Weighted Average ◽  
Real Estate Market ◽  
Real Estate Industry ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Employment Agency ◽  
Real Estate Sector ◽  
Substitution Probability

PurposePrevious research on automation and job disruption is only marginally related to the real estate industry and its characteristics. This study investigates the effects of digitization on jobs in German real estate sector, in order to assess the proportion of jobs threatened to be replaced by automation. Since Germany is the largest EU economy insights for the German real estate market allow a first approximation for Europe.Design/methodology/approachAn extensive database of the German Federal Employment Agency containing job definitions and occupation titles is matched with real estate criteria to create a subset with the relevant real estate occupations. This data is combined with a database of the German Institute of Employment Research reflecting to what extent tasks within jobs can be automated by current technical capabilities.FindingsFor the 286 identified occupations within the real estate sector a weighted average of 47 percent substitution probability through current technological capabilities is derived for tasks within the examined occupations.Practical implicationsThis contribution indicates the extent of the structural change the real estate sector has to face due to digitization: One out of two real estate jobs will have to be re-created.Originality/valueThis research quantifies the magnitude of the job killer aspect of digitization in the real estate sector.

Investor attention for retail and institutional investors: a test on the real estate market

2013 ◽  
Vol 31 (4) ◽  
pp. 314-328
Author(s):  
Gianluca Mattarocci ◽  
Georgios Siligardos
Keyword(s):  
Real Estate ◽  
Institutional Investors ◽  
Real Estate Market ◽  
Investor Attention ◽  
Causality Test ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

PurposeThe paper aims to investigate the relationship between different investor attention proxies for different types of funds (retail vs institutional ones) looking at a sample of real estate funds.Design/methodology/approachThe authors collect data about searching frequency on Google and all the news published in Italian specialized newspapers for a set of real estate funds. Following the approach proposed by Da, Engelberg and Gao, the authors construct a set of attention proxies and they compare the ranking with some summary statistics and evaluate the causality relationship among them using a Granger causality test.FindingsResults demonstrate that online search frequency is relevant for both institutional and retail funds and normally internet data are able to anticipate the news that will be published in the newspapers.Research limitations/implicationsThe analysis proposed is focused only on a small real estate market (Italy) where funds are specialized for the type of investor. A wider database can allow excluding that results achieved are biased by the specific features of the market analysed.Practical implicationsThe role of internet proxies attention measures also for institutional investors demonstrate that the managing companies offering financial instruments reserved to institutional investors should consider both channels of information – newspapers and the internet – to measure any positive or negative sign of investor attention to their products.Originality/valueThe article represents the first analysis of investor attention proxies on the real estate market and the first comparison of investor attention proxies for retail and institutional investors.

Financialisation and development: a case study of Ethiopia

2019 ◽  
Vol 11 (2) ◽  
pp. 138-196
Author(s):  
Anne Löscher
Keyword(s):  
Real Estate ◽  
Financial Development ◽  
Industrial Development ◽  
Real Estate Market ◽  
Economic Research ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Debt Levels

Purpose This paper aims to shed light on financial development in Ethiopia and its implications for overall economic development. It does so with particular focus on development understood as industrial development and with special attention drawn on inequality and debt levels as well as the real estate market in Ethiopia. Two research questions are focussed on in particular, where the first serves as prerequisite for the assessment of the second: What kind of financial development took place in Ethiopia in the past quarter of a century? Furthermore, are processes of financialisation visible in Ethiopia, and if so, to what effect? Design/methodology/approach The paper is based on publicly available macro-data and qualitative and quantitative data collected by the author herself during a three months’ research stay in Ethiopia. Findings It is found that despite higher levels of financial inclusion and deepening, industrialisation is on a relative decline. What is more, inequality and debt levels increase, and the recent growth spurts seem to be rooted in the construction sector with prices in the real estate market surging. In can be concluded that despite a flourishing financial sector, the Ethiopian economy is faced with the peril of crises associated with an inflated real estate market, inequality, debt burdens and impeded industrialisation. Originality/value African economies and, in particular, the development and effects of financial markets are still a blind spot in economic research. By combining quantitative and qualitative data on and gathered in Ethiopia, this paper therefore conducts greenfield research.

Investigating the causes of increasing consumer demand for real estate housing in Bangladesh

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazed Parvez ◽  
Sohel Rana
Keyword(s):  
Real Estate ◽  
Consumer Satisfaction ◽  
Secondary Data ◽  
Real Estate Market ◽  
Sample Size Determination ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Increasing Demand

Purpose The purpose of this paper is to find out the causes of increasing population in the real estate area. The demographic in information of the respondents and the level of satisfaction was also carried out for this study. Design/methodology/approach The authors use both primary and secondary data. Total 329 respondents were surveyed at the real estate area after completing sample size determination. Secondary data was collected from journals, real estate offices and papers. After that, using regression and correlation analysis, the data was analyzed and finalized. Findings This study identified migration as the most critical variable. The study determined ten hypotheses and only accepted two. By that, this study finds out the causes of the increasing demand of plots and flats in real estate. Originality/value This study will work as a baseline study for the real estate sector in Bangladesh. Most of the research on Bangladesh’s real estate is done mainly on real estate market assessment and consumer satisfaction. Nevertheless, this study will find out the causes of the increasing population in real estate.

Cyclical capitalization and lag vacancy

2017 ◽  
Vol 10 (2) ◽  
pp. 211-238 ◽  
Author(s):  
Maurizio d’Amato
Keyword(s):  
Real Estate ◽  
Market Value ◽  
Real Estate Market ◽  
Mortgage Lending ◽  
Property Market ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Valuation Process

Purpose This paper aims to propose a new valuation method for income producing properties. The model originally called cyclical dividend discount models (d’Amato, 2003) has been recently proposed as a family of income approach methodologies called cyclical capitalization (d’Amato, 2013; d’Amato, 2015; d’Amato, 2017). Design/methodology/approach The proposed methodology tries to integrate real estate market cycle analysis and forecast inside the valuation process allowing the appraiser to deal with real estate market phases analysis and their consequence in the local real estate market. Findings The findings consist in the creation of a methodology proposed for market value and in particular for mortgage lending determination, as the model may have the capability to reach prudent opinion of value in all the real estate market phase. Research limitations/implications Research limitation consists mainly in a limited number of sample of time series of rent and in the forecast of more than a cap rate or yield rate even if it is quite commonly accepted the cyclical nature of the real estate market. Practical implications The implication of the proposed methodology is a modified approach to direct capitalization finding more flexible approaches to appraise income producing properties sensitive to the upturn and downturn of the real estate market. Social implications The model proposed can be considered useful for the valuation process of those property affected by the property market cycle, both in the mortgage lending and market value determination. Originality/value These methodologies try to integrate in the appraisal process the role of property market cycles. Cyclical capitalization modelling includes in the traditional dividend discount model more than one g-factor to plot property market cycle dealing with the future in a different way. It must be stressed the countercyclical nature of the cyclical capitalization that may be helpful in the determination of mortgage lending value. This is a very important characteristic of such models.

A spatiotemporal exploratory analysis of real estate sales in Turkey using GIS

2019 ◽  
Vol 12 (2) ◽  
pp. 207-226
Author(s):  
Saffet Erdoğan ◽  
Abdulkadir Memduhoğlu
Keyword(s):  
Real Estate ◽  
Spatiotemporal Analysis ◽  
Real Estate Market ◽  
Investment Planning ◽  
Annual Average ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Real Estate Sales

PurposeThe purpose of this paper is to examine the real estate sales in Turkey on a district basis to reveal the current state of real estate sales and any meaningful changes in the last period. The real estate market is important and is an indicator of the country’s general economic health, as real estate is seen as an investment.Design/methodology/approachAs a powerful method of spatial analysis and evaluation, geographic information systems have been used to examine real estate data in both spatial and temporal ways. In this study, 14 years of sales data covering the years 2004 to 2017 obtained from government agencies on a district basis were evaluated using spatiotemporal methods. Several maps were produced using Getis-Ord Gi* and local Moran’s I indices, which showed the spatiotemporal change of sales and sales rates.FindingsWhen looking at the maps, provinces such as Istanbul, Ankara, Izmir, Antalya and their surrounding districts have buoyant real estate markets compared to the other side of the country. Real estate sales are more stagnant in the eastern and northern parts of the country. In addition, the authors found that the growth rate of annual average real estate sales was approximately seven times higher than the annual average population growth.Originality/valueThis spatiotemporal study, which presents 14 years of performance data of the real estate market and, by extension, the economic situation, also highlights the regions that stand out for investment planning throughout the country. The results of spatiotemporal analysis also present a new way of real estate market visualization using maps with well-designed categorizations.

Measuring behavioural biases affecting real estate investment decisions in India: using IRT

2018 ◽  
Vol 11 (4) ◽  
pp. 648-668 ◽  
Author(s):  
Richa Pandey ◽  
V. Mary Jessica
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Investment Decisions ◽  
Real Estate Market ◽  
Psychometric Test ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Level Of Agreement

Purpose This study aims to investigate the behavioural biases influencing the real estate market investing decisions of normal non-professional investors in India. Design/methodology/approach As the study involves the behavioural data with polytomous response format, psychometric test- graded response model (IRT approach) was used for the study with the help of STATA 14. Multi-stage stratified sampling was used to collect a sample of 560 respondents. The study used a 14-item scale representing behavioural biases derived from two broad behavioural theories, i.e. heuristics and prospect theories. Sample characteristics were checked using SPSS 20. Pre-required assumptions for IRT (i.e. local independence and unidimensionality) were tested by CFA using AMOS 20. Findings Five items, four of which belong to heuristics (anchoring – 2, representativeness – 1 and availability bias – 1) and one belong to prospect theory (regret aversion) are sufficient to measure the behavioural attitude of real estate investors in the Indian scenario. Item discrimination ai ranged from 0.95 to 1.52 (average value 1.29), showing moderate discrimination power of the items. The items have done a pretty good job of assessing the lower level of agreement. For the higher level of agreement, the scale came out to be less precise, with less information and higher standard error of measurement. Research limitations/implications As the behavioural biases are often false, the study suggests the investors not to repeat these nasty biases to improve investment strategies. As they are shared and not easily changeable, understanding these biases may also help them in beating the market by acting as “noise traders”. Practical implications The traditional price index is incomplete in some essential respects. The inclusion of these behavioural biases into the construction of price index will greatly improve the traditional price index, policymakers should seriously think about it. Social implications Shelter is one of the basic needs; a dwelling unit is needed for one to stay in, develop and contribute to economy and society. If investors try to minimise these biases and policymakers keep a track of these while making strategies, mispricing in this sector can be controlled to some extent, which will ultimately help in the well-being of society. Originality/value This study contributes to the limited research by investigating the behavioural biases influencing the real estate market investment decisions of normal non-professional investors. It contributes to the lacking academe on real estate market in India. The study has used a psychometric test, i.e. the item response theory, for evaluating the quality of the items.

The impact of external factors on housing prices in Lithuania

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alina Stundziene ◽  
Vaida Pilinkienė ◽  
Andrius Grybauskas
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
External Factors ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
The Impact

Purpose This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania. Design/methodology/approach The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations. Findings The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle. Practical implications Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary. Originality/value This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.

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