Investigation of Florida Housing Prices using Predictive Time Series Model

Aderibigbe, Temilola; Chi, Hongmei

doi:10.1145/3219104.3229253

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…For these analyses, the vector auto-regressive model (VAR) has been used [54][55][56][57]. Moreover, to investigate the effects of economic crises over time, predictive techniques based on regression analysis of time series have often been applied [58][59][60][61].…”

Section: Literature Reviewmentioning

confidence: 99%

An Assessment Methodology for the Evaluation of the Impacts of the COVID-19 Pandemic on the Italian Housing Market Demand

et al. 2021

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Due to the forced changes that the COVID-19 pandemic has had in many aspects of ordinary life (working, social life, training and learning in schools etc.), the characteristics of domestic spaces have significantly modified buying, selling and renting decisions in the real estate market. The aim of this research was to structure a methodology, articulated into four steps, to assess the variation that has occurred in residential market demand as a result of COVID-19 anti-contagion measures, with regard to six metropolitan Italian cities. We considered two samples of properties for each city, the first referencing the pre-COVID-19 pandemic spread and the second collected in the current situation. Consequently, we were able to determine variations in residential market appreciations through the application of a regressive econometric technique. The obtained results highlighted the relevance assumed by indoor acoustic and thermal comfort property factors, compared with the pre-COVID-19 condition. The proposed methodology could be useful to support the public and private entities involved in urban investment decision-making processes, allowing us to identify the most appreciated factors of the residential real estate market demand, in order to improve the conditions of existing and future assets and reduce the related risk levels.

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Section: Literature Reviewmentioning

confidence: 99%

An Assessment Methodology for the Evaluation of the Impacts of the COVID-19 Pandemic on the Italian Housing Market Demand

et al. 2021

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“…Both positive and negative externalities of the infrastructure are explored (e.g., transportation stops, motorway exits among the positive and crossroads or railway crossings among the negative). Paper [8] studies the housing market in Florida. Various factors are considered, such as the number of sales, employment rate, interest rate, GDP, inflation rate, hurricane strikes, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Real estate prices in the Czech Republic: behavioral approach

Hromada

2019

Engineering for Rural Development

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The article deals with the analysis and evaluation of the selected economic, social and technical parameters of real estate in the real estate market in the 119 largest cities of the Czech Republic. These cities have been selected because there is enough turnover in the real estate market. This paper will examine the relationship between the market value of property and market value of rent. The following economic and social parameters will be examined: average annual rental yield, average number of months needed to pay for an apartment, share of people in distraint, numbers of completed apartments per 1000 inhabitants per year, distance from the capital, average wages, and population migration rate. The following econometric and statistical methods are used in the article-linear regression, logarithmic regression and calculation of correlation coefficients. To obtain the input data, the software EVAL was used. EVAL software was developed by the author of this paper. EVAL is able to automatically collect all offers for sale and lease of properties in the Czech Republic that have been published on the Internet. The software has been running continuously since 2007. Data are collected and analysed from all cities in the Czech Republic in monthly periods. Every six months the database of the software expands by more than 750 000 price offers from the Internet on purchase or rent of flats, houses, commercial real estate and allotments. Recently, the author extended the functionality of the software, so that it is also able to retrieve data from the real estate cadastre. The presented results in this paper follow the previous research of the author in this area.

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“…Real estate appraisals are very important and are especially necessary for asset valuation, property taxes, insurance estimates, sales or rent transactions, risk analysis, estate planning, litigation and are relevant to government or investor decision making. Housing prices depend not only on various economic indices but also on the impact of other factors such as natural disasters, wars, market speculation, environmental variables (Aderibigbe & Chi, 2018). There are different valuation methods for real estate appraisal (Yeh & Hsu, 2018), however, in some types of real estate appraisals, a problem arises in determining real estate values.…”

Section: Introductionmentioning

confidence: 99%

Analysis of different approaches to real estate appraisal

Surgelas¹,

Arhipova²,

Pukite³

2019

Research for Rural Development

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The traditional valuation of real estate in the field of civil engineering did not include the uncertainty of human behaviour, which cannot be explained by the traditional approach. There are different valuation methods for real estate appraisal, which are basically classified into three groups as a classic, statistical and advanced. In this article, we estimated the different housing price models using the sample of 37 residential apartments in Riga, Latvia, October 2018. In order to evaluate if there is a possible association between the variables involved in relation to the property price, the analytical data were analysed by correlation analysis, analysis of variance (ANOVA), regression analysis, covariance analysis (ANCOVA), principal component analysis (PCA) and cluster analysis. The models estimation results show that using ANCOVA models for the prices forecasting the model fitting to data is less than 58%. The preliminary results of this study suggest that the estimated properties can be distributed in 4 groups, depending on number of rooms, area and age. In addition, the decision tree was created based on algorithms (J48) and a preliminary definition of the best rules was made. The decision tree presents an accuracy of 84% with 31 accepted instances for a total of 37 currently classified instances.

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Investigation of Florida Housing Prices using Predictive Time Series Model

Cited by 5 publications

References 3 publications

An Assessment Methodology for the Evaluation of the Impacts of the COVID-19 Pandemic on the Italian Housing Market Demand

An Assessment Methodology for the Evaluation of the Impacts of the COVID-19 Pandemic on the Italian Housing Market Demand

Real estate prices in the Czech Republic: behavioral approach

Analysis of different approaches to real estate appraisal

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