scholarly journals Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

2021 ◽  
Vol 7 ◽  
pp. e444
Author(s):  
Jussi Kalliola ◽  
Jurgita Kapočiūtė-Dzikienė ◽  
Robertas Damaševičius
Keyword(s):  
Real Estate ◽  
Fine Tuning ◽  
Bayesian Optimization ◽  
Ann Model ◽  
Bayesian Optimization Algorithm ◽  
Real Estate Price ◽  
Price Prediction ◽  
Property Owners ◽  
Real Estate Prices ◽  
The Empirical Analysis

Accurate price evaluation of real estate is beneficial for many parties involved in real estate business such as real estate companies, property owners, investors, banks, and financial institutes. Artificial Neural Networks (ANNs) have shown promising results in real estate price evaluation. However, the performance of ANNs greatly depends upon the settings of their hyperparameters. In this paper, we apply and optimize an ANN model for real estate price prediction in Helsinki, Finland. Optimization of the model is performed by fine-tuning hyper-parameters (such as activation functions, optimization algorithms, etc.) of the ANN architecture for higher accuracy using the Bayesian optimization algorithm. The results are evaluated using a variety of metrics (RMSE, MAE, R2) as well as illustrated graphically. The empirical analysis of the results shows that model optimization improved the performance on all metrics (reaching the relative mean error of 8.3%).

Real effects of real estate: evidence from unemployment rates

2020 ◽  
Vol 37 (4) ◽  
pp. 605-623
Author(s):  
Can Dogan ◽  
John Can Topuz
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Economy ◽  
Residential Real Estate ◽  
Content Type ◽  
Unemployment Rates ◽  
The Real ◽  
Real Estate Price ◽  
Real Effects ◽  
Real Estate Prices

Purpose This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level. Design/methodology/approach This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument. Findings The results show that changes in residential real estate prices do not have a causal effect on unemployment rates in the same quarter. However, it takes 9-12 months for an increase (decrease) in real estate prices to decrease (increase) unemployment rates. This effect is significant during both pre- and post-financial crisis periods and robust to control for the economic characteristics of MSAs. Research limitations/implications This paper contributes to the emerging literature that studies the real effects of real estate. Particularly, the methodology and the findings can be used to investigate causal relationships between housing prices and small business development or economic growth. The findings are also of interest to policymakers and practitioners as they illustrate how and when real estate price shocks propagate to the real economy through unemployment rates. Practical implications This study’s findings have important implications for academics, policymakers and investors as they provide evidence of a snowball effect associated with shocks to real estate prices: increasing (decreasing) unemployment rates following a decrease (increase) in real estate prices exacerbates the real estate price movements and their economic consequences. Originality/value This paper analyzes a significantly longer period, from 1990 to 2018, than the existing literature. Additionally, it uses the MSA-level land unavailability measure of Saiz (2010) as an instrument to explore the effects of residential real estate prices on unemployment rates and when those effects are observed in the real economy.

scholarly journals Cjenovni balon na tržištu nekretnina u Republici Hrvatskoj

2019 ◽  
Vol 9 (1) ◽  
pp. 78-88
Author(s):  
Josipa Mustać
Keyword(s):  
United States ◽  
Real Estate ◽  
Rental Housing ◽  
The United States ◽  
Significant Drop ◽  
The Real Estate ◽  
Real Estate Price ◽  
Real Estate Prices ◽  
Year 2000 ◽  
The City

The market balloons are fast-growing price phenomena, followed by their dramatic drop. In some parts of Croatia - the coastline and in the city of Zagreb, real estate prices have been growing drastically, considering the period from the year 2000. The global economic crisis occurred in the United States came 2008 due to the inflation of real estate prices, which also transferred to the Croatian economy due to the flooding effect from one market to another. This paper examines whether the same case is happening in Croatia today, namely whether the real estate price increase in Croatia was justified or they are balloons that will suddenly break. Real estate prices in Croatia are growing due to several factors, such as increased real estate demand for tourist rental, housing loans subsidies for young people and increased real estate demand by foreigners. If there is a significant drop in tourist activity in Croatia, real estate prices could fall dramatically.

scholarly journals AN INTEGRATED APPROACH BASED ON ARTIFICIAL INTELLIGENCE USING ANFIS AND ANN FOR MULTIPLE CRITERIA REAL ESTATE PRICE PREDICTION

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
A. A. Yakub ◽  
Hishamuddin Mohd. Ali ◽  
Kamalahasan Achu ◽  
Rohaya Abdul Jalil ◽  
Salawu A.O
Keyword(s):  
Real Estate ◽  
Integrated Approach ◽  
Prediction Ability ◽  
Real Estate Price ◽  
Price Prediction ◽  
Property Data ◽  
Real Estate Valuation ◽  
High Level ◽  
Error Testing ◽  
Academic Cycle

A relatively high level of precision is required in real estate valuation for investment purposes. Such estimates of value which is carried out by real estate professionals are relied upon by the end-users of such financial information having paid a certain fee for consultation hence leaving little room for errors. However, valuation reports are often criticised for their inability to be replicated by two or more valuers. Hence, stirring to a keen interest within the academic cycle leading to the need for exploring avenues to improve the price prediction ability of the professional valuer. This study, therefore, focuses on overcoming these challenges by introducing an integrated approach that combines ANFIS with ANN termed ANFIS-AN, thereby having a reiteration in terms of ANN application to fortify price predictability. Using 255 property data alongside 12 variables, the ANFIS-AN model was adopted and its outcome was compared with that of ANN. Finally, the results were subjected to 3 different error testing models using the same training and learning benchmarks. The proposed model’s RMSE gave 1.413169, while that of ANN showed 9.942206. Similarly, using MAPE, ANN recorded 0.256438 while ANFIS-AN had 0.208324. Hence, ANFIS-AN’s performance is laudable, thus a better tool over ANN.

Influence Factors of Chinese Real Estate

2013 ◽  
Vol 405-408 ◽  
pp. 3391-3395 ◽  
Author(s):  
Xue Li Wang ◽  
Shan Hua
Keyword(s):  
Real Estate ◽  
Influence Factors ◽  
Real Estate Market ◽  
Policy Recommendations ◽  
The Real Estate ◽  
Real Estate Price ◽  
The Real Estate Market ◽  
Regulation Policy ◽  
Process Of Urbanization ◽  
Real Estate Prices

This paper studied that the factors influencing Chinese real estate price. And that the land prices, the macroeconomic situation, the annual income of the households, the process of urbanization, the credit policy, the psychological expectations of consumers, the market investment or speculation, the governments regulation policy etc, Are all the influence factors of real estate prices. And the analysis results to develop appropriate policy recommendations for the healthy and sustainable development of the real estate market.

scholarly journals Real Estate Price Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 371-373
Author(s):  
Govind Kumar ◽  
◽  
Priyanka Makkar ◽  
Dr Yojna Arora ◽  
◽  
...  
Keyword(s):  
Real Estate ◽  
Real Estate Price ◽  
Price Prediction
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