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Can Economist Predict House Price Movements Essay

This essay will help determine the facts around economists predicting house price movements and the problems in doing so by studying material from the Economic Trend Annual Supplement (The National Statistics), Halifax Fact book, Forecasts from Halifax and Nationwide Appendix A. Yearly data series are collected for the AHP (average house price) and DIPH (disposable income per head) which will help create charts and analysis of house price movements between the years 1990 to 2000.

`Simple linear regression aims to find a linear relationship between a response variable and a possible predictor variable by the method of least squares’1. Therefore these charts will enable us to define the relationship between the average house price and disposable income. The result of this data will then be used to create regression to measure the association between house prices and disposable income enabling the data to conduct a hypothesis test to discover whether economist can predict house price movements.

When house price movements are observed, there are many reason that are contributing factors that are: the characteristics are: types of house (bungalows, flats terraced, detached and semi detached); types of buyer (former owner buyer & first time buyers); property ages (modern, new & old); inflation;

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interest rates; unemployment; disposable income; and affordability

The characteristics mentioned would increase or decrease the value of the property i. e.a newly built and decorated home is relatively high in price compared with the holder home which may require renovation as mentioned by Ali Anari & James Kolari. ` As a consumer good, inflation increases the construction costs of new houses through higher costs of building materials and construction wages. Higher construction costs of new houses result in higher new house prices. ‘2 In addition the value of a house will depend on the characteristics of the property.

These could be any of the following: the location within the UK; type of neighbourhood, be it council flats, mansions or retirement homes; the floors size; central heating, garage, number of bedrooms, and number of bathrooms that are available; finally whether it is leasehold or free hold; which is also supported by John M Clap & Alan E Gelfand. ‘ in addition, neighborhood amenities (and disamenities) can cause house prices to change rapidly over relatively short periods. ‘ 3 A simple example of this would be to compare houses located in London.

House prices are much higher when they are closer to the city than those of which are located in the outer skirts. Reason being is that, majority of jobs are located in the city and there would be less time to commute to work, therefore demand for housing in the city is greater, causing the value of the property to increase. ` The average house price in London now stands at  243,034, up 8. 1% on the year and against a national average of  161,746. Kensington & Chelsea is the most expensive borough with an average house price of 636,914. ‘

As shown in the Halifax house price index for Greater London in Appendix B house prices in Kensington & Chelsea in central London are 636,914 in comparison to Greenwich which is 211,300 and Croydon at 221,300. There are various models to calculate house price movements such as the ARDL model ‘The ARDL model is a co integration method for detecting the existence of long-run relationships between time series variables, as well as the subsequent estimation of their magnitude.

‘Alternatively LPR & Bayesian smoothing is another method which can be found in the Real Estate Economics. ‘The nonparametric part of our model allows sufficient flexibility to find substantial spatial variation in house values. The parameters of the kriging model provide further insights into spatial patterns. Out-of-sample mean squared error and related statistics validate the proposed methods and justify their use for spatial prediction of house values.

‘ However the method approach in this report would be to conduct the linear regression analysis to further conduct a hypothesis test as seen in Fig 1. 3. Table 1. 1 contains the data obtained from Halifax Fact book for the AHP and the DIPH from the Economic Trend Annual Supplement in Appendix A. Fig 1. 1 is a simple chart that shows a positive correlation with AHP followed by Fig 1. 2 which also shows a positive correlation for DI.

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