scholarly journals International capital movement towards the Spanish real estate sector

2020 ◽  
Vol 38 (2) ◽  
pp. 107-127
Author(s):  
Su Zhenyu ◽  
Paloma Taltavull
Keyword(s):  
Real Estate ◽  
Capital Flows ◽  
Housing Prices ◽  
Real Estate Market ◽  
Capital Movement ◽  
International Capital ◽  
Content Type ◽  
Price Bubble ◽  
Real Estate Sector ◽  
International Investors

Purpose The purpose of this paper is to examine the determinants that affect international capital flows (ICF) toward the Spanish real estate market over the period 1995 first quarter to 2017 fourth quarter. Design/methodology/approach VECM methodology is used to analyze time series and panel methods using pooled EGLS regression. Findings VECM parameter results for construction and real estate activities sectors, quickly suggesting a stable performance of capital flows toward Spanish real estate sector that the short-term fluctuation of foreign investment results contributes to the long-term equilibrium relatively soon. By applying the Monetary theory of Johnson, the model identifies a relevant role of M3 explaining capital flows to real estate, together with the lagged variables of construction and real estate activities capital flows, Spanish real interest rate and Spain’s economic growth rate; they are the significant determinants on capital movement to Spanish real estate sector. Interestingly, Spanish housing prices as an exogenous variable, directly, significantly and negatively affect real estate capital flows in all cases as a way to capture the assets price bubble. Practical implications Findings highlight reasons affecting capital flows to real estate and construction activities to Spanish sectors which allow capital Funds to take into account those drivers in their investment decisions. Originality/value This paper is the first attempt to analyze the determinants of ICF to Spanish real estate market; it has a significant meaning for both Spanish economy and international investors.

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

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.

UAE real estate market will likely recover in 2019

2018 ◽  
Keyword(s):  
Real Estate ◽  
United Arab Emirates ◽  
International Investment ◽  
Real Estate Market ◽  
Content Type ◽  
The Real Estate ◽  
Regional Conflict ◽  
Real Estate Sector ◽  
Real Estate Projects ◽  
Regulatory Measures

Significance After three difficult years, the United Arab Emirates (UAE) real estate market appears to be finding a floor, with several property consultancies and management firms cautiously optimistic over the prospects of a turnaround. New regulatory measures and a delay in some planned real-estate projects aim to support prices. Impacts The importance of the real-estate sector to Emirati non-oil GDP will rise further, magnifying its impact on growth. Dependence on international investment and public-sector spending will expose the sector to volatility in case of regional conflict. The UAE will increasingly look towards Asian countries as property buyers, especially India, China and Pakistan.

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.

scholarly journals Production and production over-supply in construction: estimating unsold stock in Italy

2014 ◽  
Vol 7 (4) ◽  
pp. 506-523 ◽  
Author(s):  
Andrea Ciaramella ◽  
Alberto Celani
Keyword(s):  
Real Estate ◽  
Poor Quality ◽  
Real Estate Market ◽  
Content Type ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Critical Issues ◽  
Definition Of

Purpose – The aim of the article is to identify the limitations and critical issues in the way information in the real estate sector in Italy is currently managed, and propose the principles of a method that would provide information and comparison of the phenomenon of over-supply and non-rational land use. This study is based on a series of assumptions, the first of which is a definition of “unsold”, deemed to mean “the amount of new housing units neither occupied nor sold nor rented”. In effect, unsold stock can be considered as over-supply of construction. Design/methodology/approach – The article identifies the critical aspects in the determination of unsold real estate in Italy, starting from the available data and research already carried out; the results are often contradictory. The comparison with programming systems of building production adopted in other countries allows identification of the guidelines that can be used to better understand and combat the phenomenon. Findings – The assessment of the state -of-the-art provides a clear picture of the shortcomings and potential of the tools used to date to meet the need of studying a complex phenomenon with many obscure points. Following the empirical analysis comes out a picture of inefficiencies due to the poor quality of information, as well as the reluctance of data-sharing and -integration procedures by the institutional and market players. Research limitations/implications – The research produces solutions addressed to the Italian situation, but it identifies systems and methods used in other countries. Practical implications – The article suggests the collection systems and management information that can be used for a more accurate knowledge of unsold real estate. Social implications – The article focuses on some of the limits of the Italian real estate market, highlighting the need for greater transparency and how this can contribute to a more conscious approach to the market. Originality/value – The article seeks to provide the necessary answers to those who must understand the reasons of harmful effects for the market, such as overproduction; besides some models focused on three areas – the procedures, the organization and the market – are also proposed.

Artificial intelligence algorithms to predict Italian real estate market prices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luca Rampini ◽  
Fulvio Re Cecconi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
Content Type ◽  
Two Cities ◽  
Artificial Neural

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

scholarly journals Capital flows and office markets in major global cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Olawumi Fadeyi ◽  
Stanley McGreal ◽  
Michael McCord ◽  
Jim Berry
Keyword(s):  
New York ◽  
Real Estate ◽  
Capital Flows ◽  
International Capital Flows ◽  
International Capital ◽  
Content Type ◽  
Cross Border ◽  
Office Markets ◽  
Office Market ◽  
Short Run

PurposeOffice markets and particularly international financial centres over the past decade have experienced rapid financialisation, developments and indeed changes in the post-global financial crisis (GFC) landscape. Importantly, the volume and types of international capital flows have witnessed more foreign actors and vehicles entering into the investment landscape with the concentration of investment intensifying within key financial centres. This paper examines the interaction of international real estate capital flows in the London, New York and Tokyo office markets between 2007 and 2017.Design/methodology/approachUsing Real Capital Analytics (RCA) data comprising over 5,700 office property transactions equating to $563bn between 2007 and 2017, the direct global capital flows into the London, New York and Tokyo office markets are assessed using an autoregressive distributed lag (ARDL) approach. Further, Granger causality tests are examined to analyse the short-run interaction of international real estate capital flows into these three major office markets.FindingsBy assessing the relativity of internal to external investments in these three central business district (CBD) office markets, differences in market dynamics are highlighted. The London office market is shown to be highly dependent on international flows and the USA, the foremost source of cross-border investment on the global stage. The cointegration and causality analysis indicate that cross-border real estate investment flows in these markets (and financial centres) show both long- and short-run relationships and suggest that the London office market remains more distinct and the most reliant on international capital flows with a wider geographical spread of investment activities and investor types. In the case of New York and Tokyo, these markets appear to be driven by more domestic investment activity and capital seemingly due to subtle factors pertaining to investor home bias, risk aversion and diversification strategies between the markets in the aftermath of the GFC.Originality/valueGiven the importance of the CBD offices in London, New York and Tokyo as an asset class for institutional investors, this paper provides some insights as to their level of connection and the interaction of the international capital flows into these three major cities.

Vietnam is at risk of real estate bubble

2019 ◽  
Keyword(s):  
Real Estate ◽  
Urban Areas ◽  
Real Estate Market ◽  
Content Type ◽  
The Real Estate ◽  
Real Estate Bubble ◽  
Global Recession ◽  
Real Estate Sector ◽  
Growing Economy ◽  
The Government

Subject Headwinds in Vietnam's real estate sector. Significance Much of the foreign investment flowing into Vietnam’s fast-growing economy is being directed at the real estate sector, especially high-end condominiums. There are indications that the government may curb foreign ownership of these luxury apartments, but the units are currently priced far above what most local people can afford. Impacts Some investors may respond to falling prices by holding on to their apartments, which would leave many residences empty in urban areas. If property demand declines, Vietnam’s central bank may turn to monetary loosening to make home loans more attractive. Vietnam’s real estate market would be vulnerable to a downturn in the event of a global recession.

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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