Measuring Determinants of House Prices: Listing Behaviour in Italian Real Estate Market

2013 ◽  
Keyword(s):  
Real Estate ◽  
House Prices ◽  
Real Estate Market

Real estate development in the city of Athens during the financial crisis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria Nikitidou ◽  
Fragiskos Archontakis ◽  
Athanasios Tagkalakis
Keyword(s):  
Financial Crisis ◽  
Real Estate ◽  
Structural Characteristics ◽  
House Prices ◽  
Real Estate Market ◽  
Structural Factors ◽  
Data Set ◽  
Content Type ◽  
Capital Financing ◽  
The City

Purpose This study aims to determine how the prices of residential properties in the Greek real estate sector are affected by their structural characteristics and by the prevailing economic factors during recession. Design/methodology/approach Based on 13,835 valuation reports for the city of Athens, covering a period of 11 years (2006–2016), this study develops a series of econometric models, taking into account both structural characteristics of the property market and the macroeconomic relevant variables. Finally, the city of Athens is divided into sub-regions and the different effects of the structural factors in each area are investigated via spatial analysis confirming the validity of the baseline model. Findings Findings show that the size, age, level, parking and storage space can explain the property price movements. Moreover, the authors find evidence that it is primarily house demand variables (e.g. the annual average wage, the unemployment rate, the user cost of capital, financing constraints and expectations about the future course of the house market) that affect house prices in a statistically significant manner and with the correct sign. Finally, using a difference-in-differences approach, this study finds that an increase in house demand (on account of net migration) led to higher house prices in smaller and older than in larger and younger apartments in areas with high concentration of immigrants. Originality/value This study uses a novel data set to help entities, individuals and policy-makers to understand how the recent economic and financial crisis has affected the real estate market in Athens.

Foreign investment in Australian residential properties

2019 ◽  
Vol 12 (2) ◽  
pp. 166-180 ◽  
Author(s):  
Hassan F. Gholipour ◽  
Hooi Hooi Lean ◽  
Reza Tajaddini ◽  
Anh Khoi Pham
Keyword(s):  
Real Estate ◽  
Foreign Investment ◽  
House Prices ◽  
Real Estate Market ◽  
Housing Development ◽  
Housing Construction ◽  
Annual Data ◽  
Content Type ◽  
Residential Properties ◽  
The Impact

Purpose The purpose of this study is to examine the impact that foreign investment in existing houses and new housing development has on residential house prices and the growth of the housing construction sector. Design/methodology/approach The analysis is based on a panel cointegration method, estimated using annual data for all Australian states and territories spanning the period of 1990-2013. Findings The results indicate that increases in foreign investment in existing houses do not significantly lead to increases in house prices. On the other hand, a 10 per cent increase in foreign investment for housing development decreases house prices by 1.95 per cent. We also find that foreign real estate investments have a positive impact on housing construction activities in the long run. Originality/value Existing studies used aggregate foreign real estate investment in their analyses. As foreign investment in existing houses and foreign investment for housing development have different impacts on the demand and supply sides of housing market, it is crucial that the analysis of the effects of foreign investment in residential properties on real estate market is conducted for each type differently.

scholarly journals Homeownership Rate and Other House Price Determinants Impacts on New House Prices in Colombia

2020 ◽  
pp. 35-50
Author(s):  
Hector Botello-Peñaloza
Keyword(s):  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Real Estate Market ◽  
The Real Estate ◽  
Homeownership Rate ◽  
Colombian Government ◽  
Price Determinants ◽  
Price Instability

Homeownership remains a preferred form of tenancy in different parts of the world. The attractions of security, stability, investment potential and a sense of pride outweigh the fear of price instability. For this reason, the Colombian government has encouraged in recent years, various demand policies that have sought to promote the increase in the number of homeowners. However, these ideas could have a severe impact on prices in the real estate market. Therefore, this study seeks to examine the effect of homeownership rate on new house prices in an emerging country with low real estate ownership, credit restrictions and average per capita income. The study uses panel data model to examine the influence of housing tenancy and other variables on the variation of housing prices in Colombia. Data were obtained from various sources including the Central Bank of Colombia, Financial Superintendence of Colombia, and National Administrative Department of Statistics of Colombia. The results show that homeownership rates have a positive effect on the price of new homes, which supports the hypothesis of the research. The population growth of the cities is the factor that is most relevant when explaining the price variations.

scholarly journals Measuring the Effect of Location on House Prices in Italy

2020 ◽  
pp. 54-62
Author(s):  
Gaetano Lisi ◽  
Mauro Iacobini
Keyword(s):  
Real Estate ◽  
House Prices ◽  
House Price ◽  
Real Estate Market ◽  
Hedonic Model ◽  
Hedonic Price ◽  
Dummy Variables ◽  
Hedonic Price Method ◽  
Housing Submarkets ◽  
Real Estate Assets

The Italian housing market is characterised by both a strong heterogeneity of real estate assets and a reduced number of property sales. These features, indeed, hamper the use of the hedonic price method, namely, the method that is mostly used for assessing the house prices and for estimating the monetary value of housing characteristics. In this paper, therefore, a hedonic model with dummy variables that identify housing submarkets is used to achieve two important results: enabling greater use of multiple regression analysis in the study of the Italian real estate market, and catching, in the simplest possible manner, the effect of location on house price. Indeed, the house's location is, together with the area in square metres, the housing characteristic that most influences the house price.

Research on Xi'an Real Estate Market and National Economy Based on Spectral Analysis

2011 ◽  
Vol 368-373 ◽  
pp. 3083-3087
Author(s):  
Hua Tang ◽  
Hui Min Li ◽  
Tao Zhou
Keyword(s):  
Spectral Analysis ◽  
Real Estate ◽  
National Economy ◽  
Statistical Data ◽  
House Prices ◽  
Real Estate Market ◽  
Real Estate Industry ◽  
Real Estate Investment ◽  
Real Estate Price ◽  
Periodic Fluctuations

Cycle fluctuations have been identified which includes real estate investment, real estate consumption, real estate industry and national economy. Spectral analysis is applied in such research based on the statistical data from 1997 to 2010 of Xi’an. The mutual relation could be found between investment and price of Xi’an real estate referring that investment increases with the rise in house prices. Furthermore, a great deal of randomness has been shown in Xi'an real estate consumption which does little effect on the cycle of real estate price indicating real estate market in Xi'an is a seller's market. At the same time, due to the fluctuation cycle of national economy in Xi’an is more than 12 years; the periodic fluctuations of national economy cannot be deduced from the periodic fluctuations of real estate in Xi’an.

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 Real estate boom in Chile and fundamentals on house prices

2020 ◽  
Vol 6 (1) ◽  
pp. 1-26
Author(s):  
C. Aguilera Alvial
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Structural Breaks ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Integration Test ◽  
The Real Estate Market

This article studies the fundamentals of housing prices based on the Real Index of Housing Prices (IRPV), given that in recent times in Chile there has been a sustained increase in price levels and seeks to find evidence on the existence of a possible speculative bubble in the real estate market. Following the methodology of various Chilean and international authors, the Engle & Granger Co-integration methodology was applied. Furthermore, the results of the previous methodology were compared using the Johansen Co-integration test. Then a method to find structural breaks is applied. As a result, evidence is found to not reject the existence of a bubble in the real estate market. It is found that only interest rates co-integrate in the long term with the evolution of house prices, while the other fundamentals present a spurious relationship.

scholarly journals International Real Estate Review

2007 ◽  
Vol 10 (2) ◽  
pp. 113-130
Author(s):  
Benoit Julien ◽  
◽  
Paul Lanoie ◽  
Keyword(s):  
Real Estate ◽  
Negative Impact ◽  
House Prices ◽  
Real Estate Market ◽  
Data Set ◽  
Long Run ◽  
Before And After ◽  
Short Run ◽  
Noise Barrier ◽  
The Impact

This paper provides the first study on the impact of noise barriers on the price of adjacent houses based on a repeat sale analysis (RSA). RSA allows us to empirically examine the differential between the prices of houses sold before and after an event that may have affected their value, and after other relevant variables such as the evolution of the real estate market and major renovations performed on the house are controlled. This paper focuses on the neighborhood of Laval, a suburb of Montreal, where a large noise barrier was built in 1990 along a highway. The data set contains transaction information on 134 houses that were sold at least twice from 1980–2000. The empirical result will show that the noise barrier induced a decrease of 6% in the house prices in our sample in the short run, while it had a stronger negative impact of 11% in the long run.

scholarly journals Spatio-Temporal Analysis of the Real Estate Market Using Geographic Information Systems

2013 ◽  
Vol 21 (2) ◽  
pp. 72-82 ◽  
Author(s):  
Piotr Cichociński ◽  
Janusz Dąbrowski
Keyword(s):  
Spatial Distribution ◽  
Real Estate ◽  
House Prices ◽  
Temporal Analysis ◽  
Real Estate Market ◽  
Geographic Information ◽  
Numerical Comparison ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

Abstract The paper proposes the use of geographic information system tools for the analysis of spatial and temporal aspects of the real estate market. In particular, it focuses on the graphical presentation of the spatial distribution of price and its variability over time. The possibility of presenting an image of the spatial distribution of prices in the form of a 3D model is studied. A topographic surface is proposed as an alternative to traditional methods of spatial interpolation. Visual verification and numerical comparison have shown its superiority over other previously used methods. The best method of presenting four-dimensional data - the variation in time of the spatial distribution of house prices - was sought. The possibility of taking time into account as one of the attributes of the analyzed and presented objects, available in advanced GIS software, was used for this purpose. The undertaken activities were based on formal guidelines for the registration of time set out in the ISO 19100 series of standards dedicated to geographic information. Potential sources of data for this kind of analysis were identified and their availability was examined. The paper also presents how to build a spatial database on the basis of the available information, which is a starting material for further analysis. The carried out research demonstrated the benefits of the spatial approach to trends of changes in real estate prices, which can be used, among others, for mass appraisal.

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