Application of Artificial Neural Network to Predict Real Estate Investment in Qingdao

2012 ◽  
pp. 213-219 ◽  
Author(s):  
Ping Zhang ◽  
Wenjing Ma ◽  
Tiejun Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Estate ◽  
Real Estate Investment ◽  
Artificial Neural

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 USE OF ARTIFICIAL NEURAL NETWORKS IN THE STATE CADASTRAL VALUATION OF LAND PLOTS

2021 ◽  
Vol 26 (5) ◽  
pp. 180-190
Author(s):  
Ekaterina N. Loseva ◽  
◽  
Natalia O. Mitrofanova ◽  
◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Real Estate ◽  
Individual Characteristics ◽  
Affecting Factors ◽  
High Rise ◽  
Increase Productivity ◽  
Artificial Neural ◽  
The Individual

At present, real estate cadastral valuation is carried out in large volumes and at regular intervals, which may reduce the objectivity and relevance of the results of such a valuation. In other words, the traditional cadastral valuations do not meet the current needs of society. The solution may be to use new techniques and technologies, such as neural networks. The automation of the cadastral valuation will reduce the estimation time, increase productivity and quality, and take into account all the individual characteristics of the property being evaluated. The subject of research is the calculation of the cadastral value of land plots using an artificial neural network. The object of this research is residential area land plots within the Novosibirsk boundaries which were divided into two segments: segment 2 "Residential constructions (mid-rise and high-rise)", segment 13 "Horticulture, low-rise residential con-structions". The tasks of the research: the determination of factors which influence the cadastral value of real estate and their differentiation; accumulation of up-to-date information about real estate; preparation of data for creation of artificial neural network. And as a result were revealed the basic and additional cost-affecting factors, which then were differentiated for further development of artifitial neural network capable of calculating the cadastral value in automated mode.

scholarly journals Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach

2020 ◽  
Vol 17 (3) ◽  
pp. 1467
Author(s):  
Sinan Adnan Diwan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Estate ◽  
Residential Real Estate ◽  
Property Market ◽  
Market Values ◽  
Estate Property ◽  
Artificial Neural ◽  
Case Base ◽  
Case Base Reasoning

<p>Real estate forecasting has become an integral part of the larger process of business planning and strategic management in real estate sector. This study covers residential estate markets and concentrates on property types, while previous studies that have considered country wide house price indices. There is a gap identified in the literature which need to study correlations between property types within a region or a city and whether they will provide diversification benefits for real estate investors such as risk reduction per unit of returns. This aim of the paper is to propose and develop a computer assisted real estate property price forecasting model. This proposed framework will examine the current uses of artificial intelligence, particularly combining case base reasoning and artificial neural network, in the business-forecasting field and considers suitable applications in real estate. The methodology consists of five phases: 1) Data gathering b) Data cleaning c) ANN Training d) CBR (Case Base Reasoning) similarity retrieval e) Result retrieval. This research will investigate the influence of residential real estate property characteristics on property values (prices) in global context, it revealed a high positive linear correlation between property characteristics and the property market values; an indication that these characteristics reasonably predict property market values. The results of the study will enable Real Estate Professionals to make fair estimates of the market values of residential real estate properties given the features/characteristics of such housing units.</p>

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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