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 Modeling the Real Estate Prices in Olsztyn under Instability Conditions

2012 ◽  
Vol 11 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Mirosław Bełej ◽  
Sławomir Kulesza
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Intrinsic Property ◽  
Small Variation ◽  
Smooth Curve ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
The Stability

Abstract The paper deals with the description of the issues related to the dynamics of the real estate market in terms of sharp, unexpected changes in the housing prices which have been observed in the last decade in many European countries due to some macroeconomic circumstances. When such perturbations appear, the real estate market is said to be structurally unstable, since even a small variation in the control parameters might result in a large, structural change in the state of the whole system. The essential problem addressed in the paper is the need to define and discriminate between the intervals of stable and unstable real estate market development with special attention paid to the latter. The research aims at modeling hardly explored field of discontinuous changes in the real estate market in order to reveal the bifurcation edge. Assuming that the periods of sudden price changes reflect an intrinsic property of the real estate market, it is shown that the evolution path draws for most of the time a smooth curve onto the stability area of the equilibrium surface, and only briefly penetrates into the instability area to hop to another equilibrium state.

scholarly journals Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

2020 ◽  
Vol 12 (1) ◽  
pp. 346 ◽  
Author(s):  
Alice Barreca ◽  
Rocco Curto ◽  
Diana Rolando
Keyword(s):  
Real Estate ◽  
Urban Areas ◽  
Housing Prices ◽  
Real Estate Market ◽  
Current Status ◽  
The Real ◽  
The Real Estate ◽  
Activity Intensity ◽  
The Real Estate Market ◽  
Spatial Autoregressive

Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.

scholarly journals Identifying Real Estate Opportunities Using Machine Learning

2018 ◽  
Vol 8 (11) ◽  
pp. 2321 ◽  
Author(s):  
Alejandro Baldominos ◽  
Iván Blanco ◽  
Antonio Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Housing Prices ◽  
Market Price ◽  
Real Estate Market ◽  
Support Vector ◽  
Regression Problem ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

scholarly journals Management of the Real Estate Market in Vietnam with Lessons from Poland

2018 ◽  
Vol 6 (6) ◽  
Author(s):  
Vu Ngoc Xuan
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Real Estate Market ◽  
Lessons Learned ◽  
Debt Ratio ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Per Capita ◽  
Bad Debt

Vietnamese economy in the year 2017 reached a GDP growth rate of 6.81%, inflation was controlled at 3.53%. According to Prime Minister Nguyen Xuan Phuc, Vietnam's economy has overcome many difficulties with the recovery and higher growth. In 2017, the size of the GDP economy will be about $ 220 billion, GDP by purchasing power parity - PPP $ 600 billion, per capita GDP of $ 2,385, and GDP per capita PPP is 6,000 US dollars. As predicted by the General Statistics Office, Vietnam's GDP in the next two years is expected to increase by 6.8%, and 7%. The exchange rate between the Vietnamese dong and foreign currencies such as the US dollar, the yen and the euro remains stable, while a trade surplus of $ 2.67 billion in 2017, slightly up from $ 2.52 billion US surplus in 2016. In addition to the macroeconomic highlights, Vietnam's economy faces challenges due to bad debt from the decline of the real estate market in the past, the bad debt ratio The banking system is high with interest rates falling but still at high levels, many businesses still find it difficult to mobilize business capital. At present, the drastic direction in the direction and management of the State Bank, the birth of the company VAMC recently brought the bad debt ratio of banks to an average of less than 5%. In this article, the author discusses the lessons learned from the management of the real estate market in Poland to provide a number of measures to increase liquidity in the real estate market in Vietnam economic growth in the future.

scholarly journals Households and real estate: Strategies of accumulation and inequality in Spain, 2002-2017

2021 ◽  
pp. 1-4
Author(s):  
Diederik Boertien ◽  
Antonio López-Gay
Keyword(s):  
Real Estate ◽  
Urban Areas ◽  
Housing Prices ◽  
Wealth Inequality ◽  
Real Estate Market ◽  
Economic Resource ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Property Owners

Real estate has traditionally been an important economic resource for Spanish households. The development of the real estate market in Spain during the 21st century brings forth two very different stories. The first story is one of obstacles to access housing. It has become increasingly hard to buy or rent a home. Housing prices have risen considerably in urban areas while people’s income changed very little. The second story is one of accumulation of properties. Housing has been, and continues to be, a form of saving, investment and speculation for small and large property-owners. Falling housing prices permitted resourceful households to accumulate more properties during the financial crisis. These two stories lead to the following question: How did changes in the ownership of properties impact inequality in Spain? In this Perspectives Demogràfiques, we analyse how developments in the real estate market are connected to wealth inequality in Spain. The results point at a polarization of access to property; both the number of households without property and the number of households with multiple properties increased over time. Because real estate is the most important form of household’s wealth, the accumulation of properties has become a non-negligible part of wealth inequality between households in Spain.

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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