A housing price index with the improvement-value adjusted repeated sales (IVARS) method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chung Yim Edward Yiu ◽  
Ka Shing Cheung
Keyword(s):  
Price Index ◽  
Simulation Analysis ◽  
House Price ◽  
Housing Price ◽  
Housing Quality ◽  
Repeat Sales ◽  
Open Market ◽  
Hedonic Price ◽  
Content Type ◽  
Price Movements

Purpose The repeat sales house price index (HPI) has been widely used to measure house price movements on the assumption that the quality of properties does not change over time. This study aims to develop a novel improvement-value adjusted repeat sales (IVARS) HPI to remedy the bias owing to the constant-quality assumption. Design/methodology/approach This study compares the performance of the IVARS model with the traditional hedonic price model and the repeat sales model by using half a million repeated sales pairs of housing transactions in the Auckland Region of New Zealand, and by a simulation approach. Findings The results demonstrate that using the information on improvement values from mass appraisal can significantly mitigate the time-varying attribute bias. Simulation analysis further reveals that if the improvement work done is not considered, the repeat sales HPI may be overestimated by 2.7% per annum. The more quality enhancement a property has, the more likely it is that the property will be resold. Practical implications This novel index may have the potential to enable the inclusion of home condition reporting in property value assessments prior to listing open market sales. Originality/value The novel IVARS index can help gauge house price movements with housing quality changes.

scholarly journals Repeat Assessed Values Model for Housing Price Index

2017 ◽  
Vol 25 (4) ◽  
pp. 25-39 ◽  
Author(s):  
Manuela Carini ◽  
Marina Ciuna ◽  
Manuela De Ruggiero ◽  
Francesca Salvo ◽  
Marco Simonotti
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Methodological Approach ◽  
Housing Price ◽  
Time Range ◽  
Repeat Sales ◽  
Hedonic Price ◽  
Index Numbers ◽  
Housing Price Index ◽  
Innovative Methodology

Abstract This study proposes an innovative methodology, named Repeat Appraised Price Model (RAV), useful for determining the price index numbers for real estate markets and the corresponding index numbers of hedonic prices of main real estate characteristics in the case of a lack of data. The methodological approach proposed in this paper aims to appraise the time series of price index numbers. It integrates the principles of the method of repeat sales with the peculiarities of the Hedonic Price Method, overcoming the problem of an almost total absence of repeat sales for the same property in a given time range; on the other hand, the technique aims to overcome the limitation of the repeat sales technique concerning the inability to take into account the characteristics of individual properties.

House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yeşim Aliefendioğlu ◽  
Harun Tanrivermis ◽  
Monsurat Ayojimi Salami
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Housing Prices ◽  
House Price ◽  
Monthly Basis ◽  
Data Set ◽  
House Price Index ◽  
Content Type ◽  
Asymmetric Pricing ◽  
Long Run

Purpose This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan. Design/methodology/approach Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis. Findings The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets. Research limitations/implications The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study. Practical implications Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision. Originality/value This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

Property investor decisions using income and rental ratio signals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Discount Rate ◽  
Price Index ◽  
Housing Prices ◽  
Housing Price ◽  
Consumer Sentiment ◽  
Content Type ◽  
Income Ratio ◽  
Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Income capitalisation method and hedonic model: an integrated approach

2019 ◽  
Vol 37 (3) ◽  
pp. 289-300
Author(s):  
Gaetano Lisi
Keyword(s):  
Real Estate ◽  
Discount Rate ◽  
House Price ◽  
Integrated Approach ◽  
Hedonic Model ◽  
Alternative Methods ◽  
Hedonic Price ◽  
Hedonic Prices ◽  
Content Type ◽  
Pull Out

Purpose The purpose of this paper is to provide an integrated approach that combines the two methods usually used in the real estate appraisals, namely, the income capitalisation method and the hedonic model. Design/methodology/approach In order to pull out the link between the income capitalisation approach and the hedonic model, the standard hedonic price function is introduced into the basic model of income capitalisation instead of the house market value. It follows that, from the partial derivative, a direct relation between hedonic prices and discount rate can be obtained. Finally, by using the close relationship between income capitalisation and direct capitalisation, a mathematical relation between hedonic prices and capitalisation rate is also obtained. Findings The developed method allows to estimate the capitalisation rate using only hedonic prices. Indeed, selling and hedonic prices incorporate all of the information required to correctly estimate the capitalisation rate. Furthermore, given the close relation among going-in and going-out capitalisation rates and discount rate, the proposed method could also be useful for determining both the going-out capitalisation rate and the discount rate. Practical implications Obviously, it is always preferable to estimate the capitalisation rate by just using comparable transactional data. Nevertheless, the method developed in this paper is especially useful when: the rental income data are missing and/or not entirely reliable; the data on rental income and house price are related to different homes; the capitalisation rate, in fact, should compare the rent and value of identical homes. In these cases, therefore, the method can be a valuable alternative to direct estimation. Originality/value The large and important literature on real estate economics and real estate appraisal neglects the relationship between hedonic prices and capitalisation rate, thus considering the hedonic model and the income capitalisation approach as two separate and alternative methods. This paper, instead, shows that integration is possible and relatively simple.

scholarly journals Developing a House Price Index for The Netherlands: A Practical Application of Weighted Repeat Sales

2007 ◽  
Vol 37 (2) ◽  
pp. 163-186 ◽  
Author(s):  
S. J. T. Jansen ◽  
P. de Vries ◽  
H. C. C. H. Coolen ◽  
C. J. M. Lamain ◽  
P. J. Boelhouwer
Keyword(s):  
The Netherlands ◽  
Price Index ◽  
House Price ◽  
Repeat Sales ◽  
Practical Application ◽  
House Price Index

Estimating the price of apartments in Tehran using extracted compound variables

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Javad Koohpayma ◽  
Meysam Argany
Keyword(s):  
Design Methodology ◽  
House Price ◽  
Housing Price ◽  
Least Square ◽  
Hedonic Method ◽  
Content Type ◽  
Ordinary Least Square ◽  
Different Types ◽  
High Inflation ◽  
Practical Implications

Purpose Housing price is a barometer of a national economy. In recent years, Iran experienced high inflation in its economy, which affects everything, including housing. The purpose of this study is the estimation of the value of residential apartments of Tehran using ordinary least square (OLS) and geographically weighted regression (GWR) methods. Design/methodology/approach This paper proposed a method for determining the compound variables and used them to estimate and evaluate the prices in the district six of Tehran city. Also, this paper compared the GWR and OLS methods with different types of factors and their influences in house price estimations. Findings During the high inflation period of the study period, the age of buildings, inflation, parking, storage room and their locations are the most critical factors that affect the price of apartments in district six of Tehran. Besides, compound variables have the most influence on the prediction of the prices. Research limitations/implications The exact location of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Practical implications The exact locations of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Originality/value The originality of the proposed method is that it used a different approach to determine the valid variables of the apartment prices. Also, the evaluation of the method showed that the proposed variables are significantly useful.

scholarly journals ARIMA modelling of Lithuanian house price index

2015 ◽  
Vol 8 (1) ◽  
pp. 135-147 ◽  
Author(s):  
Arvydas Jadevicius ◽  
Simon Huston
Keyword(s):  
Housing Market ◽  
Market Price ◽  
House Price ◽  
Investment Strategy ◽  
Price Inflation ◽  
Price Changes ◽  
Content Type ◽  
Time Series Modelling ◽  
Price Movements ◽  
Modelling Approach

Purpose – This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies. Design/methodology/approach – The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context. Findings – As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes. Research limitations/implications – Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market. Originality/value – Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies.

Impact of macroeconomic indicators on housing prices

2019 ◽  
Vol 12 (6) ◽  
pp. 1055-1071 ◽  
Author(s):  
Satish Mohan ◽  
Alan Hutson ◽  
Ian MacDonald ◽  
Chung Chun Lin
Keyword(s):  
Price Index ◽  
Time Series Data ◽  
Statistical Significance ◽  
Housing Prices ◽  
Consumer Price Index ◽  
New York State ◽  
Housing Price ◽  
Series Data ◽  
Content Type ◽  
Macroeconomic Indicators

Purpose This paper uses statistical analyses to quantify the effects of five major macroeconomic indicators, namely crude oil price, 30-year mortgage interest rate (IR), Consumer Price Index (CPI), Dow Jones Industrial Average (DJIA), and unemployment rate (UR), on housing prices over time. Design/methodology/approach Housing price is measured as housing price index (HPI) and is treated as a variable affecting itself. Actual housing sale prices in the Town of Amherst, New York State, USA, 1999-2008, and time-series data of the macroeconomic indicators, 2000-2017, were used in a vector autoregression statistical model to examine the data that show the greatest statistical significance and exert maximum quantitative effects of macroeconomic indicators on housing prices. Findings The analyses concluded that the 30-year IR and HPI have statistically significant effects on housing prices. IR has the highest effect, contributing 5.0 per cent of variance in the first month to 8.5 per cent in the twelfth. The UR has the next greatest influence followed by DJIA and CPI. The disturbance from HPI itself causes the greatest variability in future prices: up to 92.7 per cent in variance 1 month ahead and approximately 74.5 per cent 12 months ahead. This result indicates that current changes in house prices heavily influence people’s expectation of future prices. The total effect of the error variance of the macroeconomic indicators ranged from 7.3 per cent in the first month to 25.5 per cent in the twelfth. Originality/value The conclusions in this paper, along with related tables and figures, will be useful to the housing and real estate communities in planning their business for the next years.

Robust hedonic price indexes

2016 ◽  
Vol 9 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Steven C Bourassa ◽  
Eva Cantoni ◽  
Martin Hoesli
Keyword(s):  
House Price ◽  
Ordinary Least Squares ◽  
Robust Methods ◽  
Hedonic Price ◽  
Omitted Variables ◽  
Systematic Analysis ◽  
Content Type ◽  
Robust Techniques ◽  
Price Indexes ◽  
Hedonic Indexes

Purpose – The purpose of this paper is to demonstrate the application of robust techniques to the estimation of hedonic house price indexes. Design/methodology/approach – The authors use simulation analysis to compare an index estimated using ordinary least squares (OLS) with several indexes estimated using robust techniques. The analysis uses sales transactions data from a US city. The authors then explore how robust methods can correct for omitted variables under some circumstances and how they affect the revision problem that occurs when longitudinal hedonic indexes are updated. Findings – Robust methods can resolve missing variable problems in some circumstances and also can substantially reduce the revision problem in longitudinal hedonic indexes. Practical implications – Robust techniques may be preferable to OLS when constructing longitudinal hedonic indexes. Originality/value – This is the first paper to undertake a systematic analysis of the applicability of robust techniques in constructing hedonic house price indexes.

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