Predicting Days on Market to Optimize Real Estate Sales Strategy

Irregularities and frauds are frequent in the real estate market in Bulgaria due to the substantial lack of rigorous legislation. For instance, agencies frequently publish unreal or unavailable apartment listings for a cheap price, as a method to attract the attention of unaware potential new customers. For this reason, systems able to identify unreal listings and improve the transparency of listings authenticity and availability are much on demand. Recent research has highlighted that the number of days a published listing remains online can have a strong correlation with the probability of a listing being unreal. For this reason, building an accurate predictive model for the number of days a published listing will be online can be very helpful to accomplish the task of identifying fake listings. In this paper, we investigate the use of four different machine learning algorithms for this task: Lasso, Ridge, Elastic Net, and Artificial Neural Networks. The results, obtained on a vast dataset made available by the Bulgarian company Homeheed, show the appropriateness of Lasso regression.

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Identifying Real Estate Opportunities Using Machine Learning

Applied Sciences ◽

10.3390/app8112321 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2321 ◽

Cited By ~ 11

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.

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A spatiotemporal exploratory analysis of real estate sales in Turkey using GIS

Journal of European Real Estate Research ◽

10.1108/jerer-06-2018-0024 ◽

2019 ◽

Vol 12 (2) ◽

pp. 207-226

Author(s):

Saffet Erdoğan ◽

Abdulkadir Memduhoğlu

Keyword(s):

Real Estate ◽

Spatiotemporal Analysis ◽

Real Estate Market ◽

Investment Planning ◽

Annual Average ◽

Content Type ◽

The Real ◽

The Real Estate ◽

The Real Estate Market ◽

Real Estate Sales

PurposeThe purpose of this paper is to examine the real estate sales in Turkey on a district basis to reveal the current state of real estate sales and any meaningful changes in the last period. The real estate market is important and is an indicator of the country’s general economic health, as real estate is seen as an investment.Design/methodology/approachAs a powerful method of spatial analysis and evaluation, geographic information systems have been used to examine real estate data in both spatial and temporal ways. In this study, 14 years of sales data covering the years 2004 to 2017 obtained from government agencies on a district basis were evaluated using spatiotemporal methods. Several maps were produced using Getis-Ord Gi* and local Moran’s I indices, which showed the spatiotemporal change of sales and sales rates.FindingsWhen looking at the maps, provinces such as Istanbul, Ankara, Izmir, Antalya and their surrounding districts have buoyant real estate markets compared to the other side of the country. Real estate sales are more stagnant in the eastern and northern parts of the country. In addition, the authors found that the growth rate of annual average real estate sales was approximately seven times higher than the annual average population growth.Originality/valueThis spatiotemporal study, which presents 14 years of performance data of the real estate market and, by extension, the economic situation, also highlights the regions that stand out for investment planning throughout the country. The results of spatiotemporal analysis also present a new way of real estate market visualization using maps with well-designed categorizations.

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Identifying Real Estate Opportunities Using Machine Learning

10.20944/preprints201810.0297.v1 ◽

2018 ◽

Author(s):

Alejandro Baldominos ◽

Antonio José Moreno ◽

Rubén Iturrarte ◽

Óscar Bernárdez ◽

Carlos Afonso

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 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.

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Machine Learning House Price Prediction

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst061236 ◽

2020 ◽

Vol 6 (12) ◽

pp. 186-189

Author(s):

Manu Shahi, Abhay Singh, Amita Goel Vasudha Bahl and Nidhi Sengar

Keyword(s):

Machine Learning ◽

Real Estate ◽

Learning Algorithms ◽

House Price ◽

Machine Learning Algorithms ◽

Lasso Regression ◽

The Real ◽

The Real Estate ◽

The People ◽

Real Estate Prices

This document present the implementation of Machine Learning algorithms for the prediction of the house and the real estate prices. As the house and real estate prices are subject to change with the market conditions, so it become very difficult to predict the real estate prices with the conventional methods as it may sometimes gives some exaggerated result that may incur losses. To predict the prices more accurately and precisely we predict the prices based on the statics of that particular area which has all the trends and factors on which the price is dependent. To analyse these data , several algorithms are used namely random forest, linear regression , lasso regression etc. Use of these algorithms decreases the margin of error and more precise result are achieved. So,we at this point recommend the real estate agents and house vendors as well as the people to look into the model for better valuation of the house. This model can also be integrated with the real estates websites to give better recommendation based on the prices using Machine Learning Algorithms.

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