scholarly journals REVEALING THE INVESTMENT VALUE OF PENANG HERITAGE PROPERTIES

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Tiong Cheng Chin ◽  
Bin Tan Yan ◽  
Fang Wong Wai ◽  
Seng Lai Kong ◽  
Yu Xuan Koh
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Regression Method ◽  
Hedonic Regression ◽  
Social Functions ◽  
Diversification Strategy ◽  
Aesthetic Quality ◽  
Alternative Investments ◽  
Property Price ◽  
Heritage Buildings

Heritage buildings are a representation of historic features and the Malaysian culture. The intangible value of a heritage property comprises aesthetic quality, spiritual aspects, social functions, and its own uniqueness. Therefore, heritage properties have been seen to be moving away from traditional alternative investments, which are not covered by conventional real estate schemes. Additionally, the characteristics of heritage properties are expected to be seen as ‘art’, and they offer a highly beneficial diversification strategy with a relatively low correlation towards traditional assets classes. The Penang (Island) Heritage Property Price Index (PPHPPI) is estimated to be using a hedonic regression method. Based on the index, the heritage property records the highest quarterly returns and risk among the conventional assets considered in this study.

A Regression Method for Real Estate Price Index Construction

1963 ◽  
Vol 58 (304) ◽  
pp. 933-942 ◽  
Author(s):  
Martin J. Bailey ◽  
Richard F. Muth ◽  
Hugh O. Nourse
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Regression Method ◽  
Index Construction ◽  
Real Estate Price

Predicting property price index using artificial intelligence techniques

2019 ◽  
Vol 12 (6) ◽  
pp. 1072-1092 ◽  
Author(s):  
Rotimi Boluwatife Abidoye ◽  
Albert P.C. Chan ◽  
Funmilayo Adenike Abidoye ◽  
Olalekan Shamsideen Oshodi
Keyword(s):  
Artificial Intelligence ◽  
Real Estate ◽  
Price Index ◽  
Policy Formation ◽  
Support Vector ◽  
Ann Model ◽  
Property Market ◽  
Content Type ◽  
Property Prices ◽  
Property Price

Purpose Booms and bubbles are inevitable in the real estate industry. Loss of profits, bankruptcy and economic slowdown are indicators of the adverse effects of fluctuations in property prices. Models providing a reliable forecast of property prices are vital for mitigating the effects of these variations. Hence, this study aims to investigate the use of artificial intelligence (AI) for the prediction of property price index (PPI). Design/methodology/approach Information on the variables that influence property prices was collected from reliable sources in Hong Kong. The data were fitted to an autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM) models. Subsequently, the developed models were used to generate out-of-sample predictions of property prices. Findings Based on the prediction evaluation metrics, it was revealed that the ANN model outperformed the SVM and ARIMA models. It was also found that interest rate, unemployment rate and household size are the three most significant variables that could influence the prices of properties in the study area. Practical implications The findings of this study provide useful information to stakeholders for policy formation and strategies for real estate investments and sustained growth of the property market. Originality/value The application of the SVM model in the prediction of PPI in the study area is lacking. This study evaluates its performance in relation to ANN and ARIMA.

Real-Estate Derivatives

2017 ◽  
Author(s):  
Radu S. Tunaru
Keyword(s):  
Real Estate ◽  
Financial Markets ◽  
Investment Banks ◽  
Financial Instruments ◽  
Stress Tests ◽  
Real World Data ◽  
No Arbitrage ◽  
New Frontier ◽  
Property Price ◽  
Comparative Description

This book brings together the latest concepts and models in real-estate derivatives, the new frontier in financial markets. The importance of real-estate derivatives in managing property price risk that has destabilized economies frequently in the last hundred years has been brought into the limelight by Robert Shiller over the last three decades. In spite of his masterful campaign for the introduction of real-estate derivatives, these financial instruments are still in a state of infancy. This book aims to provide a state-of-the-art overview of real-estate derivatives at this moment in time, covering the description of these financial products, their applications, and the most important models proposed in the literature in this area. In order to facilitate a better understanding of the situations when these products can be successfully used, ancillary topics such as real-estate indices, mortgages, securitization, and equity release mortgages are also discussed. The book is designed to pay attention to the econometric aspects of realestate index prices, time series, and also to financial engineering no-arbitrage principles governing pricing of derivatives. The emphasis is on understanding the financial instruments through their mechanics and comparative description. The examples are based on real-world data from exchanges or frommajor investment banks or financial houses in London. The numerical analysis is easily replicable with Excel and Matlab. This is the most advanced published book in this area, combining practical relevance with intellectual rigour. Real-estate derivatives will become important for managing macro risks in order to pass stress tests imposed by regulators.

scholarly journals Compilation of Commercial Property Price Indices for Germany Tailored for Policy Use

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Thomas A. Knetsch
Keyword(s):  
Time Series ◽  
Real Estate ◽  
Commercial Real Estate ◽  
German Population ◽  
Price Indices ◽  
Commercial Property ◽  
Price Increases ◽  
Comprehensive Data ◽  
Property Price ◽  
Existing Data

Abstract The compilation of commercial property price indices (CPPIs) is challenging. Policymakers urge for timely, reliable and comprehensive data. In Germany, lack of data prevents the calculation of official figures by the national statistical authority. Different applications of price indices need different definitions of commercial real estate. CPPIs according to these definitions are constructed on the basis of existing data for 127 German towns and cities (that cover about one-third of German population). The overall price developments revealed by the various indices are rather similar in terms of central time series characteristics, while differences in detail can be explained by their specific compositions. Price increases for all definitions have been strongest in the seven largest cities. The definitions tend to lead to more marked differences for medium-sized towns.

scholarly journals Comparative analysis in residential property price level and price dynamics in urban and rural areas of Hajdú-Bihar County

2014 ◽  
pp. 85-90
Author(s):  
Ákos Fischl
Keyword(s):  
Real Estate ◽  
Rural Development ◽  
Rural Areas ◽  
Price Volatility ◽  
Sustainable Rural Development ◽  
Macroeconomic Factors ◽  
Macroeconomic Indicators ◽  
Property Price ◽  
Real Estate Prices ◽  
Urban And Rural Areas

In modern market economies residential real estate prices, price shifts and their correlations with macroeconomic factors are surveyed quite frequently. However, in contrast with the wide scope of foreign examples, so far existing analyses in Hungary have ignored examining relation and extensity of macroeconomic indicators and failed to examine their effect on real estate pricing. The scope of this survey is to highlight these potential correlations and thus develop new aspects of analysis. Although the examination needs further extension both in time and space, the results of this survey may help to understand the importance of the responsible management of the most precious element of national wealth from the perspective of sustainable rural development. Based on my preliminary results, there exists a strong correlation between the number of inhabitants of a settlement and the average real estate prices. Nevertheless, the correlation seems to be significant only for cities. In the case of smaller settlements the correlation still exists but at a lower level. As opposed to the results of former publications and my own expectations, no direct link could be tackled between the amount of income tax paid by private individuals and real estate prices either in the cities or in the villages within the territory and time span examined in my analysis. Although this correlation is measurable on a macro-economic level, my micro-regional analyses revealed the complexity of asset pricing and price volatility. Continuing this survey, my goal is to identify the hidden factors influencing real estate prices, whose thorough mapping may promote conscious rural development.

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