Can Economist Predict House Price Movements
CAN ECONOMIST PREDICT HOUSE PRICE MOVEMENTS?
WHAT ARE THE PROBLEMS THEY FACE IN DOING SO?
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; 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.' 4 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.'5 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.' 6
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.
Table 1.1
Year
Average
Disposable
House Price
Income Per Head
990
69,478
8,712
991
68,505
8,857
992
65,935
9,084
993
64,267
9,317
994
65,874
9,434
995
66,786
9,632
996
69,889
9,845
997
77,531
0,230
998
86,835
0,235
999
96,340
0,537
2000
09,446
1,163
Fig 1.1
Fig 1.2
LINEAR CORRELATION
` The technique of correlation measures the strength of the association between the variables.'7 Fig 1.3 illustrates a positive correlation result indicating a close trend line between price and disposable income which suggests that the association is very strong. X-axis is the disposable income the independent variable and Y-axis is the house price dependant variable.
Fig 1.3
`the variable we are trying to predict is the dependent variable while we are using as a basis for prediction is the independent variable'8 Fig1.4 illustrates the linear regression results which are derived from AHP (average house price the dependent variable) and RDI (real disposable income independent variable. The equation of the slope indicates that the average growth of house price is 17.2% per year and the forecast as to when DI reaches 15,000 the AHP would be 167,200.
Fig 1.4
Year
Average
Real Disposable
House Price
Income Per Head
990
69,478
8,712
991
68,505
8,857
992
65,935
9,084
993
64,267
9,317
994
65,874
9,434
995
66,786
9,632
996
69,889
9,845
997
77,531
0,230
998
86,835
0,235
999
96,340
0,537
2000
09,446
1,163
Y
X
Pearson's correlation efficient (using correl R ...
This is a preview of the whole essay
Fig 1.4
Year
Average
Real Disposable
House Price
Income Per Head
990
69,478
8,712
991
68,505
8,857
992
65,935
9,084
993
64,267
9,317
994
65,874
9,434
995
66,786
9,632
996
69,889
9,845
997
77,531
0,230
998
86,835
0,235
999
96,340
0,537
2000
09,446
1,163
Y
X
Pearson's correlation efficient (using correl R )
0.87486
Pearson's correlation efficient (using pearson r )
0.87486
slope of regression equation (b)
7.22596
intercept of regression equation (a)
-91189.44250
coefficient of determination (r-square)
0.76538
forecast for x = 15,000
£167,199.92
REAL RATES
Table 1.2 illustrates that the real interest rate is very much determined upon the calculation of the RPI inflation in relation to the interest base rate. For example year 2000 shows RPI of 3.0% and interest base rate 6% which results to a 3% actual rate for that year and 1995 shows 3.5% RPI and 6.5% base rate resulting to actual rate of 3%. In order to keep the actual rate low there has to be an increase in base rate to counter for the high inflation. However if the real rates are the same during the course of many years, this does not mean that you will be paying the average rate over that period, as the base and inflation rate will be high or low. Although the fundamentals are the same, mortgage lending is different, compared with the inflation rate. In fact due to slightly higher inflation rate in 1995 it was more likely that home owners were paying slightly more than what they were in 2000. ( )
Table 1.2
Year
RPI
Interest
Real
Inflation
Base Rate
990
9.5
4.0
4.5
991
5.9
0.5
4.6
992
3.7
7.0
3.3
993
.6
5.5
3.9
994
2.4
6.3
3.9
995
3.5
6.5
3.0
996
2.4
6.0
3.6
997
3.1
7.3
4.2
998
3.4
6.3
2.9
999
.5
5.5
4.0
2000
3.0
6.0
3.0
Fig 1.5
LINEAR REGRESSION
'The basic idea of regression is prediction, with the simplest case being that of predicting one continuous variable from another.' 9 In other words linear regression is used to make predictions about a single value. Simple linear regression involves discovering the equation for a line that most nearly fits the given data. That linear equation is then used to predict values for the data. It is clearly understood that if we take the analysis of the DIPH, this shows that during the period of 1990 - 2000 the disposable income per head has increased over the years, which results in a greater spend value therefore an increase in house prices, as more properties are on the market. Due to this the summary output shows Pearson correlation efficient (r) has an increase from 87.4% to 90.7% and the coefficient of determination has increased from 0.77% to 0.83% for the 10-year term. Disposable income and year together now contribute 82% to the average house price leaving 18% unexplained. Therefore an additional variable contributes to the improving of the regression equation. This means the regression equation should be more accurate in predicting AHP for a given year and disposable income.
Table 1.3
AHP
RDIPH
Year
69,478
8,712
990
68,505
8,857
991
65,935
9,084
992
64,267
9,317
993
65,874
9,434
994
66,786
9,632
995
69,889
9,845
996
77,531
0,230
997
86,835
0,235
998
96,340
0,537
999
09,446
1,163
2000
Fig 1.6
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.907
R Square
0.823
Adjusted R Square
0.779
Standard Error
6996.890
Observations
1
ANOVA
df
SS
MS
F
Significance F
Regression
2
820726680
910363340.2
8.595
0.001
Residual
8
391651765.3
48956470.67
Total
0
2212378446
Coefficients
Standard Err
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-46553.937
39779.867
-1.170
0.276
-138286.535
45178.660
-138286.535
45178.660
RDIPH
4.309
3.442
4.157
0.003
6.372
22.247
6.372
22.247
Year
-185.236
14.823
-1.613
0.145
-450.020
79.547
-450.020
79.547
HYPOTHESIS TEST
A hypothesis test is an unproved proposition to explain certain fact or observation. It is not a prediction itself but will allow making prediction of the relationship between the AHP and DI in this test. `An hypothesis is a prediction of the relationship between an independent variable and dependent variable in an experiment.10
`The null hypothesis, H0 represents a theory that has been put forward, either because it is believed to be true or because it is to be used as a basis for argument, but has not been proved. The alternative hypothesis, H1, is a statement of what a statistical hypothesis test is set up to establish.'11
Ho there is no relation between AHP & DI and economist cannot predict house price movements.
Ha there are relations between AHP & DI & economist can predict future house price movement
The critical value has already been calculated in the summer work out sheet and the significant value is 0.003. The test statistic is however highlighted also in the disposable income as 4.157 as illustrated in fig 1.6.
When the significant value is < 0.5 then we would reject the null hypothesis as there is no sufficient evidence to support Ho, consequently rejecting Ho. As a result of the hypothesis test there is a relation between house price and disposable income and economist can predict house price movements.
PROBLEMS WITH PREDICTING HOUSE PRICE MOVMENTS
When the property has a value after considering the type, age & location there are various factors that would cause the average house price to increase and decrease and they are as follows: inflation; interest Rates; unemployment; disposable income; affordability
'The traditional way of explaining inflation has been to treat it as the result of excessive demand, i.e., of excess demand pulling prices up. The value of aggregate supply and demand must be equal.' 12 The rise of interest rates reduces the demand for housing which will ease the house price inflation reducing the average house price as seen on table 1.1, where the interest increased to 6.3 and the AHP decreased to 65,267. When interest rates rise, this causes AD (aggregate demand) to shift to the left due to the decrease in demand. ''The fall in interest rates can also be expected to ease the financial constraints on the personal sector a little and the saving ratio may gradually begin to drift downwards, although there is unlikely to be a sharp turnaround until lower interest rates have been sustained for some time.'13 When table 1.2 is analysed it can be seen that the real for each year between 1990 and 2000 has been calculated. During the year 1995 and 2000 the real rate was 3.0. Although the fundamentals are the same mortgage lending is different compared with inflation, this does not make it more affordable.
Inflation can be caused by various reasons such as high oil prices, when the UK joined the EEC, war in other countries affecting UK food and retail price and high demand for housing etc. `Monetary and fiscal policies have succeeded in lowering inflation in industrial countries but the broader challenge of macroeconomic stabilization remains in two important respects. One is minimizing boom and bust cycles in economic activity and their disruptive effects on the financial system. The other is to keep at bay inflationary pressures while also preventing the emergence of its converse namely, generalised price deflation.'14
There are three types of inflation: menu cost inflation which involves adjusting price list or labels associated with cost; productions that are related to the constant rising cost; and finally inflation that is created by continual rise in aggregate demand.15
Through the fiscal or the monetary policies the government can try to and control inflation by increasing or decreasing interest rates and reducing low unemployment etc. 'Monetary policy is not well equipped alone to deal with regional asset price booms. Fiscal and regulatory policies thus have a potentially important role to play.'16 In spite of this, there are some factors that are beyond the governments control, such as increase in oil prices, war in other countries which affects the UK food and retail prices. Consequently these type of events can make it more difficult to control inflation, as was seen previously with the Kuwait war in 1990, where inflation was at 9.5.
In conclusion when the data was used to perform the regression analysis test, it can be seen in fig 1.3 that there are strong relationships between AHP and DI with perfect correlation. After conducting the hypothesis test, the result was to reject the null hypothesis which was to facilitate. There were no relation between AHP and DI and that it was not possible for economist to predict house price movements. `2003 prediction? Hometrack predicted modest growth of 4% in 2003, but this was an over estimate. According to its latest data available, house prices in November had risen by 1.1% year-on-year.'17
However as there were no sufficient evidence to support this HO was rejected, consequently the decision concluded that there was a relation between AHP and DI, as a result economist may be able to predict house price movements.
Although the test concludes that economist can predict house price movements there are problems they may have to deal with, such as inflation, rise of interest rates, unemployment etc. 'Interest rates are currently at 4.75%, compared with 15% in 1991, incomes have increased, as have employment levels, and mortgage lenders are lending out higher multiples of income.'18
'Assuming base rates remain on hold, it said, inflation will pick up next year and rise to the Bank's target rate of 2% in two years. '19 On the other hand the government can use monetary and fiscal policies to tackle these problems and once the economy is stable the economist may be able to predict house price movements.
REFERENCES
JOURNALS
Publication Information: Article Title: Predicting Spatial Patterns of House Prices Using LPR [Local Percentile Rank] and Bayesian Smoothing. Contributors: John M. Clapp - author, Alan E. Gelfand - author, Hyon-Jung Kim - author. Journal Title: Real Estate Economics. Volume: 30. Issue: 4. Publication Year: 2002. Page Number: 505+. COPYRIGHT 2002 American Real Estate & Economics Association; COPYRIGHT 2003 Gale Group
Publication Information: Article Title: House Prices and Inflation. Contributors: Ali Anari - author, James Kolari - author. Journal Title: Real Estate Economics. Volume: 30. Issue: 1. Publication Year: 2002. Page Number: 67+. COPYRIGHT 2002 American Real Estate & Economics Association; COPYRIGHT 2002 Gale Group
Publication Information: Article Title: The Run-Up in Home Prices: A Bubble. Contributors: Dean Baker - author. Journal Title: Challenge. Volume: 45. Issue: 6. Publication Year: 2002. Page Number: 93+. COPYRIGHT 2002 M.E. Sharpe, Inc.; COPYRIGHT 2003 Gale Group
Publication Information: Article Title: The UK Economy. Contributors: Nigel Pain - author. Journal Title: National Institute Economic Review. Issue: 142. Publication Year: 1992. Page Number: 10+. COPYRIGHT 1992 National Institute of Economic and Social Research; COPYRIGHT 2002 Gale Group
Publication Information: Article Title: Asset Prices and the Business Cycle. Journal Title: World Economic Outlook. Publication Year: 2000. Page Number: 77. COPYRIGHT 2000 International Monetary Fund; COPYRIGHT 2002 Gale Group
WEB PAGES
http://www.stats.gla.ac.uk/steps/glossary/paired_data.html#simplinregr 11/11/04
http://cne.gmu.edu/modules/dau/stat/regression/linregsn/nreg_1_frm.html 11/11/04
http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#hypothtest 11/11/04
ei.cornell.edu/student/glossary.asp 11/11/04
www.Hbosplc.com 28/10/04
www.nationwide.co.uk/hpi 11/11/04
http://www.nationwide.co.uk/hpi/methodology.htm 20/10/04
http://news.bbc.co.uk/1/hi/business/3999607.stm 10/11/2004
http://news.bbc.co.uk/1/hi/business/3735782.stm 10/11/04
http://news.bbc.co.uk/1/hi/business/3333605.stm 10/11/04
http://www.themovechannel.com/sitefeatures/news/2004-october/4a.asp 07/10/04
ARTICLES
Financial times
By Scheherazade Daneshkhu Published: November 10 2004
GUARDIAN
Larry Elliott
Wednesday December 17, 2003
By Mark Atkinson and Charlotte Denny
Wednesday March 24, 1999
David Walker
Monday June 28, 1999
BOOKS
Essential Quantitative Methods for Business Management & Finance-Les Oakshot-2nd Edition-2001-page 205
Statistics-Frank Owen & Ron Jones-4th edition-Pitman Publishing-1994
Lawrence Erlbaum Associates - D. Delaney - Scott E. Maxwell - 1999
Intermediate Macroeconomics - Output, Inflation, and Growth - Thomas Mayer, D. C. Rowan - Norton. Place of Publication New York - 1972. Page: 332.
Economics- John Sloman-5th Edition- 2003
APPENDIX
A Halifax fact book (TABLE 1 & 5)
Economic Trend Annual Supplement 1.5
B Halifax house price index for Greater London
Appendix A
Households' real
RPI
Average
disposable
Households'
Unemployment
inflation
Real GDP
earnings
income
expenditure
Number
rate
PSNCR
$/£
Euro/£
FTSE
% y-o-y
% y-o-y
% y-o-y
% y-o-y
% y-o-y
mn
%
% of GDP
00
976
6.6
2.7
5.9
-0.4
0.5
.1
3.9
6.4
.81
.51
977
5.9
2.4
8.7
-1.9
-0.4
.2
4.2
3.7
.75
.44
978
8.2
3.3
3.2
7.0
5.3
.1
4.1
5.2
.92
.43
979
3.4
2.7
5.2
6.0
4.6
.1
3.8
4.7
2.12
.48
980
8.0
-2.1
20.7
.7
0.0
.4
4.8
5.2
2.33
.62
981
1.9
-1.4
3.1
-0.4
0.1
2.2
7.6
3.3
2.03
.78
982
8.6
.9
9.4
-0.4
0.9
2.5
9.0
3.2
.75
.77
983
4.5
3.5
8.3
2.0
4.1
2.8
9.9
3.2
.52
.71
984
5.0
2.6
6.0
3.6
2.2
2.9
0.1
3.1
.34
.70
,232
985
6.0
3.6
8.2
3.5
3.8
3.0
0.3
.6
.30
.71
,413
986
3.4
4.0
8.1
4.1
6.4
3.1
0.5
0.9
.47
.48
,679
987
6.1
4.6
7.7
3.6
5.5
2.8
9.4
-0.7
.64
.40
,714
988
4.9
5.0
8.8
5.5
7.6
2.3
7.6
-3.0
.78
.50
,793
989
7.8
2.2
9.2
4.8
3.4
.8
5.9
-1.3
.64
.47
2,423
990
9.5
0.8
9.8
3.4
.0
.6
5.5
-0.1
.79
.37
2,160
991
5.9
-1.4
7.7
2.1
-1.5
2.3
7.6
2.3
.77
.40
2,420
992
3.7
0.2
6.1
2.8
0.5
2.7
9.2
5.9
.77
.33
2,832
993
.6
2.3
3.0
2.8
2.9
2.9
9.7
7.1
.50
.26
3,429
994
2.4
4.4
3.7
.4
3.1
2.6
8.8
5.3
.53
.27
3,066
995
3.5
2.9
3.1
2.4
.6
2.3
7.6
4.3
.58
.19
3,689
996
2.4
2.8
3.6
2.3
3.6
2.1
7.0
2.9
.56
.21
4,119
997
3.1
3.3
4.2
4.1
3.6
.6
5.4
0.1
.64
.45
5,132
998
3.4
3.1
5.2
0.3
3.9
.3
4.6
-0.8
.66
.49
5,883
999
.5
2.9
4.8
3.3
4.4
.2
4.2
-0.9
.62
.52
6,930
2000
3.0
3.9
4.5
6.1
4.6
.0
3.6
-3.9
.52
.64
6,223
2001
.8
2.3
4.4
4.9
2.9
.0
3.2
0.3
.44
.61
5,217
2002
.7
.8
3.6
.5
3.3
0.9
3.1
2.1
.50
.59
3,940
2003
2.9
2.2
3.3
2.1
2.3
0.9
3.1
3.6
.63
.45
4,477
Table 5: Mortgage Rate, Base Rate and Savers' Rate
end
Building Society Average Mortgage
Real mortgage
Base
Real base
Savers' rate
Real savers'
Mortgage rate differential with
Mortgage rate differential with
period
rate (1) %
rate (2) %
Rate %
rate (2) %
(gross) (3) %
rate (2) %
base rate %
savers rate' %
976
1.1
-5.6
4.0
-2.6
9.7
-6.9
-2.9
.3
977
1.1
-4.9
7.5
-8.4
9.3
-6.6
3.6
.7
978
9.6
.4
2.5
4.3
8.4
0.2
-3.0
.2
979
1.9
-1.5
7.0
3.6
0.8
-2.7
-5.1
.2
980
4.9
-3.1
4.0
-4.0
3.3
-4.8
0.9
.6
981
4.0
2.1
4.5
2.6
2.2
0.3
-0.5
.8
982
3.3
4.7
0.0
.4
1.8
3.2
3.3
.5
983
1.0
6.5
9.0
4.5
9.7
5.2
2.0
.3
984
2.2
7.2
9.5
4.5
0.5
5.4
2.7
.7
985
3.0
7.0
1.5
5.5
1.7
5.6
.5
.4
986
2.3
8.9
1.0
7.6
0.9
7.5
.3
.4
987
0.3
4.2
8.5
2.4
8.7
2.5
.8
.7
988
2.8
7.8
3.0
8.1
0.9
6.0
-0.3
.8
989
4.4
6.7
5.0
7.2
2.7
5.0
-0.6
.7
990
4.3
4.9
4.0
4.5
2.9
3.4
0.3
.4
991
1.4
5.5
0.5
4.6
9.7
3.8
0.9
.7
992
9.0
5.2
7.0
3.3
6.3
2.5
2.0
2.7
993
7.9
6.4
5.5
3.9
5.3
3.7
2.4
2.6
994
7.8
5.4
6.3
3.8
5.6
3.2
.5
2.2
995
7.5
4.0
6.5
3.0
5.2
.8
.0
2.2
996
6.5
4.1
6.0
3.6
4.5
2.1
0.5
2.0
997
7.6
4.4
7.3
4.2
6.1
2.9
0.3
.5
998
7.3
3.9
6.3
2.8
6.0
2.6
.0
.3
999
6.5
5.0
5.5
4.0
5.0
3.4
.0
.5
2000
6.7
3.7
6.0
3.0
5.5
2.5
0.7
.2
2001
5.2
3.4
4.0
2.2
3.8
2.0
.2
.4
2002
5.0
3.3
4.0
2.3
3.6
.9
.0
.4
Appendix B
AVERAGE HOUSE PRICES IN GREATER LONDON (ALL PROPERTIES)
Prices shown in the tables below are arithmetic average prices of houses on which an offer of mortgage has been granted. These prices are not standardised and therefore can be affected by changes in the sample from quarter to quarter. Figures include properties sold for £1 million plus.
BOROUGH
AVERAGE HOUSE
PRICE - £
2003*
AVERAGE HOUSE
PRICE - £
2004*
%
CHANGE
Barking-and-Dagenham
49,274
66,457
2%
Barnet
298,348
350,034
7%
Bexley
82,638
95,947
7%
Brent
250,617
293,075
7%
Bromley
246,657
278,859
3%
Camden
408,097
452,159
1%
Croydon
206,641
221,015
7%
Ealing
259,390
290,936
2%
Enfield
215,920
229,691
6%
Greenwich
87,506
211,300
3%
Hackney
233,490
239,611
3%
Hammersmith-and-Fulham
369,361
433,296
7%
Haringey
248,552
276,999
1%
Harrow
273,990
282,515
3%
Havering
201,854
224,008
1%
Hillingdon
217,673
239,699
0%
Hounslow
263,397
288,239
9%
Islington
328,274
377,329
5%
Kensington-and-Chelsea
538,756
636,914
8%
Kingston-upon-Thames
260,526
308,615
8%
Lambeth
259,218
253,692
-2%
Lewisham
88,648
201,487
7%
Merton
256,944
295,782
5%
Newham
72,211
88,411
9%
Redbridge
217,625
245,636
3%
Richmond-upon-Thames
362,713
421,016
6%
Southwark
225,842
258,541
4%
Sutton
215,929
224,143
4%
Tower-Hamlets
232,623
241,857
4%
Waltham-Forest
84,620
98,393
7%
Wandsworth
310,385
366,652
8%
Westminster
439,445
498,128
3%
* 12 months to September.
Q3 2004
Pre 1919
£
919-45
£
946-60
£
Post 1960
Not new
£
New
£
All
£
Terraced
362,287
236,171
204,675
254,756
282,721
290,585
Semis
538,233
309,490
248,269
258,420
*
339,733
Detached
*
627,758
*
567,109
*
636,956
Bungalows
*
260,041
*
*
*
274,556
Flats
264,947
205,007
68,838
201,736
255,759
230,283
ALL
334,085
281,880
218,531
241,880
273,445
285,337
* Insufficient sample.
http://www.stats.gla.ac.uk/steps/glossary/paired_data.html#simplinregr
2 Article Title: House Prices and Inflation. Contributors: Ali Anari - author, James Kolari - author. Journal Title: Real Estate Economics. Volume: 30. Issue: 1
3 Article Title: Predicting Spatial Patterns of House Prices Using LPR [Local Percentile Rank] and Bayesian Smoothing. Contributors: John M. Clapp - author, Alan E. Gelfand - author, Hyon-Jung Kim - author. Journal Title: Real Estate Economics. Volume: 30. Issue: 4. Publication Year: 2002. Page Number: 505
4 John Coupe, Regional Manager, Halifax Estate Agency, Halifax house price index Greater London
5 Article Title: House Prices and Inflation. Contributors: Ali Anari - author, James Kolari - author. Journal Title: Real Estate Economics. Volume: 30. Issue: 1. Publication Year: 2002. Page Number: 67+. COPYRIGHT 2002 American Real Estate & Economics Association; COPYRIGHT 2002 Gale Group
6 Publication Information: Article Title: Predicting Spatial Patterns of House Prices Using LPR [Local Percentile Rank] and Bayesian Smoothing. Contributors: John M. Clapp - author, Alan E. Gelfand - author, Hyon-Jung Kim - author. Journal Title: Real Estate Economics. Volume: 30. Issue: 4. Publication Year: 2002.
7 Essential Quantitative Methods for Business Management & Finance-Les Oakshot-2nd Edition-2001-page 205
8 Statistics-Frank Owen & Ron Jones-4th edition-Pitman Publishing-1994
9 Harold D. Delaney, Scott E. Maxwell; Lawrence Erlbaum Associates, 1999
0http;//ei.cornell.edu/student/glossary.asp
1 http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#hypothtest
2 Book Title: Intermediate Macroeconomics: Output, Inflation, and Growth. Contributors: Thomas Mayer - author, D. C. Rowan - author. Publisher: Norton. Place of Publication: New York. Publication Year: 1972. Page: 332.
3 Publication Information: Article Title: The UK Economy. Contributors: Nigel Pain - author. Journal Title: National Institute Economic Review. Issue: 142. Publication Year: 1992. Page : 10
4 Publication Information: Article Title: Asset Prices and the Business Cycle. Journal Title: World Economic Outlook. Publication Year: 2000. Page Number: 77.
5 Economics- John Sloman-5th Edition- 2003
6 Publication Information: Article Title: Asset Prices and the Business Cycle. Journal Title: World Economic Outlook. Publication Year: 2000. Page Number: 77.
7 http://news.bbc.co.uk/1/hi/business/3333605.stm
8 http://www.themovechannel.com/sitefeatures/news/2004-october/4a.asp
9 http://news.bbc.co.uk/1/hi/business/3999607.stm 10/11/2004
