scholarly journals ASSESSING THE IMPACT OF CRIME ON RESIDENTIAL PROPERTY MARKETS

2014 ◽  
Author(s):  
Chris Eves
Keyword(s):  
Residential Property ◽  
The Impact ◽  
Property Markets

scholarly journals Estimating Residential Property Values on the Basis of Clustering and Geostatistics

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 143 ◽  
Author(s):  
Beata Calka
Keyword(s):  
Estimation Error ◽  
Local Property ◽  
Percentage Error ◽  
Residential Property ◽  
Property Value ◽  
Second Stage ◽  
Spatial Factor ◽  
Using Data ◽  
The Impact ◽  
Property Markets

The article presents a two-stage model for estimating the value of residential property. The research is based on the application of a sequence of known methods in the process of developing property value maps. The market is divided into local submarkets using data mining, and, in particular, data clustering. This process takes into account only a property’s non-spatial (structural) attributes. This is the first stage of the model, which isolates local property markets where properties have similar structural attributes. To estimate the impact of the spatial factor (location) on property value, the second stage involves performing an interpolation for each cluster separately using ordinary kriging. In this stage, the model is based on Tobler’s first law of geography. The model results in property value maps, drawn up separately for each of the clusters. Experimental research carried out using the example of Siedlce, a city in eastern Poland, proves that the estimation error for a property’s value using the proposed method, evaluated using the mean absolute percentage error, does not exceed 10%. The model that has been developed is universal and can be used to estimate the value of land, property, and buildings.

scholarly journals International Real Estate Review

1998 ◽  
Vol 1 (1) ◽  
pp. 45-63
Author(s):  
Edward Ng ◽  
Keyword(s):  
High Rate ◽  
High Price ◽  
Residential Property ◽  
Price Setting ◽  
Economic Determinants ◽  
Price Response ◽  
Natural Choice ◽  
High Price Level ◽  
The Impact ◽  
Property Markets

Prices in the Asian residential property markets have skyrocketed over the past decade. A high rate of economic growth is one of the major reasons for the price spiral. Most Asian residential property markets are, however, concentrated and national in nature. Maintaining an artificially high price level through coordination amongst producers is not impossible and would be the natural choice of oligopolistic behavior (Scherer and Ross, 1990). This study examines price responses to changes in economic determinants in Singapore. The focus is on supply. Cointegration and error-correction techniques are employed to test if upward and downward adjustment speeds are similar. The results verify the impact of GDP growth, but also show that price response to the supply of housing units is significantly downward rigid. This is not inconsistent with the hypothesis of collusive price setting by property developers.

The Impact of Landfills on Residential Property Values

1992 ◽  
Vol 7 (3) ◽  
pp. 297-314 ◽  
Author(s):  
Alan Reichert ◽  
Michael Small ◽  
Sunil Mohanty
Keyword(s):  
Property Values ◽  
Residential Property ◽  
Residential Property Values ◽  
The Impact

Analysing the Residential Market in Trójmiasto Using Self-Organizing Map

2021 ◽  
Author(s):  
Olgun Aydin ◽  
Krystian Zielinski
Keyword(s):  
Multidimensional Data ◽  
Residential Property ◽  
Self Organizing Map ◽  
Property Market ◽  
Residential Properties ◽  
Price Trends ◽  
Residential Market ◽  
Key Aspects ◽  
The Impact ◽  
Self Organizing

Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

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