House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

Limsombunc, Visit; Gan, Christopher; Lee, Minsoo

doi:10.3844/ajassp.2004.193.201

Cited by 207 publications

(172 citation statements)

References 31 publications

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“…In the direct real estate literature, the performance of neural networks is less conclusive. The quality of the house price predictions obtained with this technique is supported by some researchers (Nguyen and Cripps 2001;Limsombunchai et al 2004;Peterson and Flanagan 2008), but criticized by others (Worzala et al 1995;Lenk et al 1997).…”

Section: Literature Reviewmentioning

confidence: 91%

Are Securitized Real Estate Returns More Predictable Than Stock Returns?

Serrano

Hoesli

2008

Book of Abstracts: 15th Annual European Real Estate Society Conference - Krakow, Poland

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This paper examines whether the predictability of securitized real estate returns differs from that of stock returns. It also provides a cross-country comparison of securitized real estate return predictability. In contrast to most of the literature on this issue, the analysis is not based on a multifactor asset pricing framework as such analyses may bias the results. We use a time series approach and thus create a level playing field to compare the predictability of the two asset classes. Forecasts are performed with ARMA and ARMA-EGARCH models and evaluated by comparing the entire empirical distributions of prediction errors, as well as with a trading strategy. The results, based on daily data for the 1990-2007 period, show that securitized real estate returns are generally more predictable than stock returns in countries with mature and well established REIT regimes. ARMA-EGARCH models are found to have portfolio outperformance potential even in the presence of transaction costs, with generally better results for securitized real estate than for stocks.

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Section: Literature Reviewmentioning

confidence: 91%

Are Securitized Real Estate Returns More Predictable Than Stock Returns?

Serrano

Hoesli

2008

Book of Abstracts: 15th Annual European Real Estate Society Conference - Krakow, Poland

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“…Pudaruth's research also concluded that limited number of instances in data set do not offer high prediction accuracies [1]. Limsombunchai [4] concluded in his research that neural networks are better in estimating price of a house. His method offered higher prediction accuracy as compared to hedonic method.…”

Section: Related Workmentioning

confidence: 99%

“…The limitation of this research was that it offered strong evidence of prediction superiority but did not talk of forecasting capability between the two methods used. Also, actual house prices are missing in the research and only estimated prices were used avoiding difficulties of data collection [4].…”

Section: Related Workmentioning

confidence: 99%

Vehicle Price Prediction System using Machine Learning Techniques

Noor¹,

Jan²

2017

IJCA

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This paper presents a vehicle price prediction system by using the supervised machine learning technique. The research uses multiple linear regression as the machine learning prediction method which offered 98% prediction precision. Using multiple linear regression, there are multiple independent variables but one and only one dependent variable whose actual and predicted values are compared to find precision of results. This paper proposes a system where price is dependent variable which is predicted, and this price is derived from factors like vehicle's model, make, city, version, color, mileage, alloy rims and power steering. General TermsMachine Learning.

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“…In his research Rahadi, et al [14] divide these factors into three main groups, there are physical condition, concept and location. Physical conditions are properties possessed by a house that www.ijacsa.thesai.org can be observed by human senses, including the size of the house, the number of bedrooms, the availability of kitchen and garage, the availability of the garden, the area of land and buildings, and the age of the house [15], while the concept is an idea offered by developers who can attract potential buyers, for example, the concept of a minimalist home, healthy and green environment, and elite environment.…”

Section: A House Price Affecting Factorsmentioning

confidence: 99%

Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia

Alfiyatin¹,

Febrita²,

Taufiq³

et al. 2017

ijacsa

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Abstract-House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. There are three factors that influence the price of a house which include physical conditions, concept and location. This research aims to predict house prices based on NJOP houses in Malang city with regression analysis and particle swarm optimization (PSO). PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction. The result from this research proved combination regression and PSO is suitable and get the minimum prediction error obtained which is IDR 14.186.

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House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

Cited by 207 publications

References 31 publications

Are Securitized Real Estate Returns More Predictable Than Stock Returns?

Are Securitized Real Estate Returns More Predictable Than Stock Returns?

Vehicle Price Prediction System using Machine Learning Techniques

Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia

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