Chapter 1: Introduction
This chapter is the introduction to the dissertation, and is aimed to provide the insight on how this dissertation is structured. The research study is an econometric study. The research study is based on an investigation of the relationship between housing prices and other external variables in the housing market. The research study is based on a time serial analysis which involves the calculations of correlation analysis, ratios and regression.
Housing prices show considerable fluctuation in the market. However, the housing market can be very lucrative because people desire to have their own houses or flats. Houses/flats also signify an individuals' wealth.
However if you pass by a newspaper stand you are very likely to observe a headline about the British housing market. There is little doubt that the British have an enthusiasm for stories about the housing market. Frequently, these headlines concern the level of house prices, the rate at which they are growing and the direction in which they are moving.
This study focuses on UK house prices since the late 1975s. Such is the volatility of UK house prices, it might be that when you read this research study a more appropriate title could be: ‘UK house prices: Is the roof caving in?'
Figure 1.1 shows the upward curve of house price levels across the entire period. The series is referred to as a nominal or a current price series because no adjustment has been made for inflation. Every value is the house price level observed at the time. The issue of adjusting for inflation is pursued later in the study
According to government figures, the number of owner-occupied households in England fell by 83,000 from 14.621 million in 2006 to 14.538 million in 2007. This was the largest annual fall on record. It is also the second successive year in which the number of owner-occupiers in England has fallen. Similarly, the rate of owner-occupancy dropped during 2007 from 70.3% to 69.8% in 2006; the lowest rate since 1998. At overall level the number of owner-occupiers in England fell by a record 830,001 in 2007 .This was the second successive annual decline in the number of owner-occupiers .The number of households renting privately rose further with an increase 107,000 in 2007 .There was a sharp fall of 235,000 in the number of owner-occupiers below the age of 44 between 2005 and 2006. London recorded the largest decline in owner-occupation between 2001 and 2006 with an 111,000 (6.3%) fall .The number of owner-occupiers in the north rose by 345,000 (5.1%) between 2001 and 2006 compared with a 92,000 (-1.2%) fall in the south. Castle Point in Essex has the highest owner-occupation rate in England with 88.5% of households owning their own home (http://firstrung.co.uk/articles.asp?pageid=NEWS&articlekey=9053&cat=44-0-0).
The prices at which transactions have taken place will always have an important influence on the economic behaviour of an individual and the economy as a whole. Rising house prices make individuals wealthier as the worth of their house increases. However, changes in house prices make a strong impact on the consumer's expenditure: wages, wage settlements, regional migration decisions and ultimately Government monetary policies. At the national level the fall in owner-occupancy in 2007 was driven by a 2.0% (164,000) fall in the number of those buying a home with a mortgage. The number of those owning their home outright (i.e. having either bought with cash or paid off their mortgage) failed to increase sufficiently to offset this decline, rising by only 1.3% (81,000). The number of owner-occupied households has risen by just 85,000 (0.6%) during the past five years. (http://firstrung.co.uk/articles.asp?pageid=NEWS&articlekey= 9053&cat=44-0-0)
The determination of house prices is therefore of particular interest for econometric research. There are large numbers of researchers who take an interest in the models that help them to forecast house prices. This can be at government level, investors in property, private sector and financial institutions who strengthen the mortgage market.
It is always worthwhile to examine the relationship between variables of the housing market in order to understand more detail how the housing market functions. The research study takes a time serial analysis at national level from 1975 to 2008.
The report also investigates house price modelling, making a distinction between the ‘long run' and ‘short run' behaviour of the housing market. The research study is carried out in order to analyse the relationship between the variables such as interest rate, individual income, GDP, retail price index, etc., and house prices.
The research question
It is always interesting to know how UK house prices relate to other economic variables. There are three research questions, which are: (i) how do UK house prices relate to inflation rates?; (ii) do UK house prices relate to GDP (gross domestic product):? (iii) does income have a positive linkage to housing prices? (iv) does the housing market create wealth for the nation?
The aim and the objectives
The aim of the research study is to understand in depth the relationship between English house prices and the economic variables. Previous research data showed a mixed relationship between the English house prices and the other economic variables. This research study is also aimed at analysing a particular time series. There are three objectives for this research study. These are: (i) to examine the theoretical framework and conceptual model in relation to house prices and economic variables; (ii) to examine the factors of demand and supply in the housing market; (iii) to calculate the relationship between the house prices and economic variables; and (iv) to predict the next five years' housing business trends in England.
The importance of this research
This research aims to provide an insight into the relationship between English house prices and economic variables based on time series analysis. It is important for investors to know the future house market trends in order to achieve much higher investment returns. The housing market creates a dual function as an article of trade and also investment (Foley, 2002). Consumer behaviour towards the housing market is important (Muellbauer and Murphy, 1997), and there is a need to identify the trend of business trade. It is worthwhile to know the rate of return of housing prices to income before investing in the housing market (Muelbauer and Murphy, 1997). The Financial accelerator models (Bermanke, Gertler and Gilchrist, 1999) and the BGG model are used to analyse the trends in the housing market. Using a poll-and-poll method is useful for analysing house prices (Chesterton Humberts report, 2009). Hendry (1984); Poterba (1984); Roche (1999); Bacon and MacCabe (2000) and Foley (2002). Muellbauer and Murphy (1997) demonstrated the relationship between house prices and income ratios. However, the market is uncertain and the answers were not satisfactory. This needs further investigation in relation to these variables by using different techniques such as correlation analysis and regression analysis to identify the answers. The demand for houses in the property market is determined by the interest rate in the market and the owner earnings. According to Meen (1990, 1993, 1996), mortgage rationing is linked to house prices. Meen (1993) surveys use a sample from 1959 to 1981 to analyse the condition of mortgage stock, including the proxy of mortgage rationing, nominal interest rate and the rate of house prices. Hendry (1984) uses an estimated house price equation to identify the financial wealth by using the same variables. Hendry (1984) also used an inverted house demand equation to analyse the financial wealth. The author intends to explore housing market prices in order to understand in depth the trends in the housing business market.
The chapters structured
The research report consists of five chapters. Chapter 1 is the introduction, which provides insight to the reader about how the research is structured. Chapter 2 is the literature review which involves the analysis of literature data. Literature data is based on previous researchers who have investigated similar research areas. Literature data is reviewed and used to analyse the specific areas that can be used to make recommendations. Chapter 3 is the methodology. The methodology involves an analysis of choosing suitable research method/methods for research process. Chapter 4 is findings and analysis. Findings and analysis involve collecting primary data based on the numeric calculations which use correlation analysis, ratio analysis and regression analysis to define the results. A discussion section analyses the causes and facts which are based on the research objectives. Chapter 5 is the conclusion and recommendations. This chapter is to conclude the research study. The chapter also involves three suggestions for the next five years' housing business trend.
Chapter 2: Literature Review
This chapter comprises the literature review which aims to collect data from previous researchers. A literature review is important because it can contain unexpected answers by the researcher which can be very useful for recommendations. The research review is based on the issues of theoretical frameworks and conceptual models in relation to the housing market.
Current property prices in England and Wales
The average current residential property in England and Wales was £163,352 which was estimated in June 2009. The estimate showed that there had been a 0.1% rise in residential housing prices. It was the first month that had showed increase in house prices since April 2008. The house prices' annual contraction also showed an increase from 14.3% to 16% in July 2009. However, on average, house prices showed a decline of £34, 298 since January 2008 (Chesterton Humberts report, 2009).
According to Chesterton Humberts report (2009), by using poll and polls matter, there was a fall in house price in the current property market. They used five of the eight indices to analyse the down turn in house prices. The analysis showed that almost 58% of regions in England showed a decline in house prices and The North West of England showed the biggest decline of -0.7%. In Wales, however, there was a rise in house price by 0.5% in the month of June 2009, but prices were still falling over the year by -11.6%. There was an increase in house price in London by 0.4% last June, 2009. This gave an increase of £1,088 to the average houses' value. However, flat prices increased by 0.7% in the month of June, 2009. Figure 2.1 is a summary table showing the average residential property price; month on month change and year on year change against the types of houses and flats; all based on the houses actually sold. The report showed that terraced property; and flat, apartment and maisonette increased in the month of July, 2009.
Influences on Property prices
It is believed that when council tax is re-structured, debt levels and real house prices show extremely strong growth. However, when the rate of income falls, the rate of investment return turns into a negative figure. When households are more cautious about the investment returns, mortgage lenders try to tighten up by implementing lending criteria. When income ratio is increasing, the wealth is increasing too. However, this requires an analysis of spending on assets which are improved by financial liberalisation. It needs a high level of efficiency stages in order to avoid the nominal interest rate falling. When real interest rates are high, house prices recover. However, there is no evidence of this analysis because the trend of house prices and income ratios between 1989 and 1995 is unpredictable. Muellbauer and Murphy (1997) also demonstrated the relationship between house prices and income ratios. Other researchers such as Hendry (1984); Poterba (1984); Roche (1999), Bacon and MacCabe (2000) and Foley (2002) did similar works.
Foley (2002) conducted a review of Irish house prices, to examine the reasons for the rapid growth of housing prices. The study used a supply and demand model to show house price movement and found that the establishment of house prices was the fundamental reason for causing a bubble in the property market. Roche (1999) also examined the speculative bubble in the residential property market in the UK. Foley (2002) demonstrated the trend of house prices by using a log-linear housing model which is based on demand and supply factors. The analysis takes into the accounts of: (i) the impact of investors; (ii) the influences on changes in prices of house prices and volumes of mortgage lending. Foley (2002) also used real second hand house prices to analyse the movement of the change in house prices. The analysis showed that the movement of the house prices performed well compared with the model until 1997 and then the parameters started to decline. The calculations show that the movement of the change of house prices can be modelled by using the above variables.
Foley (2000) conducted a further investigation into the movement of house prices to use the more structured model he had used for analysing Irish house prices in 1998. The analysis showed that government decisions can and do influence the movement of house prices in the market. The government reduced the capital gains tax. The analysis also showed that time changes the elasticity of house prices' income.
The demand and the supply in the housing market
The demand for houses in the property market is determined by the interest rate in the market and the amount people are earning. It is believed that with a high level of personal income growth, more people will purchase houses in the market. Houses are treated as assets for people. People with good income earnings can afford to pay mortgages and to get loans from financial institutions and building societies. However, mortgages or loans are secured against the housing property. In the financial market, there are different types of flexibilities on offer to borrowers. Financial institutions or building societies offer variable repayment in order to solve borrowers' problems. Before 1981, the interest rate was the issue. However, interest rate controls were liberalised in stages. There was very little relationship between housing equity and mortgage equity withdrawal in the mid 1980s. However, when building societies become dominant in the mortgage market, they implemented the rationing of mortgages,which diminished equity and homebuyers had to leave their house and this creates a large amount of transaction costs (Miles, 1992). Mortgage lenders were centralised in 1985 and foreign banks were looking for ways to back up their business activities. A new Building Societies Act was passed in 1986 and this gave an opportunity for building societies to finance themselves by using partially wholesale deposits. This also encourages building societies to be able to compete with other banking institutions.
According to Meen (1990, 1993, 1996), mortgage rationing is linked to house prices. The analysis was based on the relationship between the relative size of the housing stock and numbers of households to determine the financial wealth. Meen's (1993) surveys showed a very persuasive linkage during the period 1959 to 1981. He used the condition of mortgage stock, including the proxy of mortgage rationing, nominal interest rate and the rate of house prices. Dick (1990) also used a similar concept which was adopted from the Meen model. Other survey such as Hendry (1984) use estimated house price equations to identify the financial wealth by using same variables. Hendry (1984) also used an inverted house demand equation to analyse the financial wealth.
Mortgage markets in the UK have shifted with a major change in the late 1980s and early 1990s. As a result, mortgages are no longer rationed. Many empirical models of house demand have shown real change. Meen (1990) found a method to calculate mortgage rationing measurement. The analysis showed the movement of building societies from acting as mortgage suppliers and the setting of interest rates to one in which they make sure mortgage activities run smoothly. According to Rowlands (2006), property sector, accumulative assets; decision of level of consumption; and monetary policy are all related to the measure of mortgage rationing.
However Meen's (1993) analysis did not link directly to the measurement of mortgage market rationing because most of the models show no sign of an adequate mortgage rationing measurement. Meen (1993) used the data of O'Herlihy and Spencer (1972), Mayes
(1979), Hadjimatheou (1976), and HM Treasury to analyse mortgage rationing and the housing market. The analysis using these models found that building societies did not reach profits maximisation. However, building societies play a key role in providing finance to the housing sector, although the banks have provided more finance in recent years. Building societies creates the supply of mortgage funds from savers to meet the demand of households in the market.
The calculations of Meen's analysis showed that mortgage demand can be determined. According to Kearl (1979), the fit of payment mortgage create the changing from the correct shape. It is believed that inflation does not weaken the household's real financial position. It, does however, increase the households burden of paying debts. The problem can be resolved when the inflation creates higher income. When inflation occurs in the market, consumers tend to reduce their expenses. Fleming (1973) used a conventional analysis to measure the credit rationing based on consumption demand.
However, the inflation rate can affect the house buyers's ability to pay their initial payments, maturity and mortgage risk characteristics. Kearl (1979) showed a housing market model which is based on an approximately structural and reduced form and explains the inflation changing the correct shape of relative house prices in the housing sector. The analysis showed that there is no relationship between the interest rates and the housing prices. The house prices have connection with the initial payment and the time period of payment stream.
According to Titman (1982) who examined the effects of anticipated inflation based on the housing market equilibrium, the analysis showed that inflation creates the change from the correct shape of the housing market. This is because of a non-indexed tax system. The tax system used to allow tax exemption on the interest only part of the payments which increased investment return for households. This creates the movement of increasing housing demand and at the same time, it increases the real price. However, several empirical data showed a negative relationship between stock returns and the inflation rate (Rowlands, 2006). Figure 2.5 shows the average residential property price and annual property price inflation in England and Wales. The analysis is made by Chesterton and Humberts (2009) and shows that there is no strong relationship between two variables. According to Rowland (2006), when housing demand is increasing the real price of housing is increased. However, there is no relationship between investment returns and the inflation rate in housing market. The use of equilibrium model is to examine and monitor the change of inflation rate in the housing market. The analysis shows that when there is an increase in real housing price, the real housing rental price declined. Stein (1995) creates a trade in housing market model which relates to fluctuation of housing process and trading in growing house market. However, there is not solution found due to the efficient market in housing.
Shiller (1989; 1990) cited that house prices can be identified based on the past record, changing prices in housing market. The empirical data is based on the calculations such as rent to price analysis and cost to price ratio to define the results. Stein's theory on the housing market explains the relationship between house price and trading activity. The model is used to define the effects on the housing market. The model is used to analyse the housing prices and housing demand in the market. The model also explains the real after tax interest rates in relation to housing demand and house prices. However, according to Genesove and Mayer (1993), housing market is affected by only a few factors related to housing prices and housing demand.
Regional house market
The housing market in the UK is uncertain. The price of houses fluctuates from one period to another. However, the fluctuation rate is different from one region to another. It means that every region has its own prices market. Some regions may have very strong fluctuations. According to Muellbauer and Cameron (2001), the housing price cycle is led by London and the South East and spreads out to other regions of the country. A non formal analysis is used to analyse the behaviour of housing market by some researchers (David Hendry, 1984; and Guissane and Hadjimatheou, 1991). According to the UK government statistics (www,housing.odpm.gove.uk/statistics), the average dwelling price in England dramatically increased from 1969 to 1999 and the trend of housing prices in England showed a significant shift upward.
Other Theoretical frameworks and modelling
There are various variables that can form a valid co-integrating vector (Drake, 1993). Some analysts use Johansen procedures to define a valid co-integrating vector. Johanson procedure allows establishing more than one distinct co-integration vectors by using a multivariate setting. Drake (1993) decided to use a sample series which begins at 1981 in order to avoid the problem of different period issues such as pre and post mortgage rationing eras. Ashworth and Parker (1997) also support this concept.
In the UK, there are different methods that explain the UK house prices indices. In the early years UK house price models were based on demand and supply equations. The price was identified when both supply curve and demand curve intersect at a period of time. The models are those developed by such as Whitehead (1974); Hadjimatheou (1976); Mayers (1979); Hendry (1984), which determine the housing price by the outcome from economic factors. The models also define the demand and supply of the housing prices by using an equilibrium equation. The approaches identify the trend of the supply and demand in the housing market both in long run and in the short run.
From economic theory perceptions, a long run housing demand is influenced by the market housing price and various demand variables, including income, the price of substitute goods. Substitute goods involves rented accommodation, the mortgage price, interest rates and location factors. The supply of housing in the long run also depends on the market demand which investors can identify and the expected profits that they can make from house building activity. However, the decisions of construction companies are also influenced by the current real price of housing in the market, the costs of production, and the availability of planning consent. Costs of production include land, material, labour and the movement of interest rates. It is believed that when house prices in the market are increasing, the numbers of houses bought for investment purposes are also increasing. At the beginning, investors may achieve abnormal profits. In the short run, the housing price in the market is almost the same and also shows that the numbers of new housing completions are considered small. To analyse the long run demand and supply in the housing market, countries such as the UK use co-integration techniques to identify the answers. These techniques have been used by Engle and Granger (1987). Johansen (1988) uses very similar techniques but they are more advanced. Other researchers use demand and supply equations rather than co-integration techniques for their analysis (Whitehead, 1974; Mayes, 1979; and Henry, 1984).
However, the models of Whitehead (1974) and Mayes (1979) were criticised because the models failed to forecast the early boom housing market in 1970s and the model showed parameter instability. The Whitehead model showed instability when two additional observations were added into the original model, and the result showed that the model was not stable and could not be used. The counter argument to this statement was that the model should use further analysis for forecast purposes. Mayes Model also failed to analyse the rapid growth in housing prices in the early 1970s. Mayes concept disagreed with many other researchers by suggesting that building societies were the key factor causing house prices to rise in the market. This is because building societies provide funds to borrowers. This increases the level of housing demand in the market. He also argued that the house price rises in 1975 are not due to the housing price itself. Hence, the increasing house prices cannot be explained solely by the increased availability of building society funds.
The Hendry (1984) model referred to has a stylish type of analysis. The analysis is based on housing price data in relation to inflation, income household, housing stock exchange, mortgage value and individual wealth. The Hendry model framework is based on the equilibrium between supply and demand paradigms. The model assumed that housing is an assets and that it is also a service. The model also includes the issues of finance to determine the house price and future house price effects. The model is satisfactory for all diagnostic texts. However, it is not possible for the residual test. The Hendry model (1984) showed a positive growth of house prices in the housing market by using static and dynamic theory.
According to Nellis and Longbottom (1981), the Mayer model has been improved and has been used for parameter stability and randomness of residual in developing a house price model. The model has been used for the supply and demand approaches. Nellis and Longbottom (1981) cited that real income is the key factor that can determine house prices in the market. They argued that supply side influences the house prices in the market and is relatively small.
Buckley and Ermisch (1982) approach a portfolio model in relation to housing and financial assets by using the tools of supply and demand equilibrium concept. The model is used to analyse the government policy factors and the effect on house prices. The model shows that an effective macro-economic policy can influence the inflation rate in the market. Reducing inflation rate in the market will reduce the house prices in the market. Buckley and Ermisch (1983) cited that the Nellis and Longbottom model has a strong relationship between disposalable income and house prices. The model has a lack of information of anticipated inflation and location factors.
Another model was built by Giussani and Hadjimatheou (1990; 1991) based on the original works from: Whitehead (1974); Mayes (1979); Hendry (1984); Nellis and Longbottom (1981) and Engle and Granger (1987) by using a co-integration approach. The approach is based on a long run house price determination. The model shows that Henry model (1984) based on a short run specification was superior on statistical diagnosis. Giussani and Hadimatheou (1990; 1991) built a model based on long run determination of house prices on the supply side, including variables such as individual disposal income per capital; costs of house; total households; and housing stock in the market. The analysis shows that in the short run house prices are influenced by: changes in house prices, the inflation rate, the housing stock; the degree of building society funds availability, bank influences; the quantity of housing completions and the household ratio. Guissani and Hadjimatheou (1990: 1991) argued that house price is influenced by the Government monetary policy both in the short run and the long run. This includes other factors such as individual disposal income, market interest rate and the issues of taxation reflecting on mortgage interest payment.
Drake (1993) applied the Johansen model using a co-integrating vector. The analysis is taken using variables reflecting the long run equilibrium specification in the UK house prices index. The analysis also includes; individual real disposable income; building society influences on average mortgage rate; private sector housing; and a dummy variable as well as the issues of dual tax relief on mortgage. Drake (1993) has built a parsimonious model. For the short term specification, Drake (1993) left out the factors of real personal disposable income and building society average mortgage rate of market demand and supply house prices.
Another model for analysing house prices in the housing market was built by Porterba (1984). The Poterba Model (1984) uses a conventional asset-market model approach in which he uses two equation analyses. The two equations are: (i) the real house price is equal to the present value of the future net cash flow of housing service by using a discounting factor and the real tax interest rate; (ii) the new construction in the market. Poterba (1984) cited that inflation rate and the dual tax relief increases real house prices. According to his analysis, there was 30% increase in the average real house price in the market.
According to Meen (1989; 1990), the researcher used the analysis of 1980s house prices. He used his switching model regime which is incorporated under the conditioning of rationing and inflation rate effected to house prices. The Meen model identified that there was a large degree of effect between the relationship of the condition of rationing and inflation and the mortgage queues compared with the real house price in the market. According to the Meen Model, normal mortgage repayment increases although the interest rate may not be changed. This means that the mortgage institutions play a very important role in the change. However, the rise of nominal repayment may cause problems of borrower cash flow. It is because the nominal repayments tend to be higher that the borrowers' nominal income.
Modelling regional house prices has been used actively in the last few decades. However, a lot of data is referred to the relationship between new house prices and the UK market house prices. According to McAvinchey and Mclennan (1982) using micro-oriented approaches to model regional house prices, demand on the privately owned housing is influenced by the inflation
The UK housing market is pretty complex place with many characteristics and aspects affecting its everyday movements. The Housing market performs a dual function as an article of trade and investment (Foley, 2002), besides providing somewhere to live. The housing sector creates a higher percentage of net present value than any other positive feature. In the last few years, housing sector has boomed. Housing costs in the UK were very high. People buy houses because they are safety nets for people. Investing in housing is a long term investment. Houses cannot be easily moved away.
It is difficult to measure housing prices and it is still the case because of conceptual and practical problems. This is because housing is a heterogeneous asset. The value of a house only can be determined when it sells. In the UK, there are seven types of house indices and three types of concepts which contain: (i) the value of house transaction; (ii) the price of house characteristics; (iii) the housing stock value. However, indices are based on different data for different methods. It is believed that house pricing is a complex issue and can be misled by using house price inflation. When the housing sector becomes a very lucrative investment, many analysts and investors pay attention and investigate it. The investigation was carried out more than 40 to 50 years ago. In the 1970s and 1980s there were booms in the UK property market. However, the property market in the UK was in down turn in 1990s. According to Muellbauer and Murphy (1997), housing booms and busts is because of the consumer behaviours. Figure 2.1 shows the distribution of average transacted flat and maisonette prices in localities in England and Wales in 2002, quarter 3. The relative frequency is influenced by inflation rates and housing prices. However, the weighting scheme depends on house index and house values.
In the UK, the housing price is measured differently in housing purchase transaction. The indices begin with house prices through to the sale prices. The house price may be influenced by the mortgage lender. Figure 2.2 is a house purchase time line and housing price indices between the buyer and seller. A timeline may be changed and a mortgage may be renegotiated.
However, some econometric models were not concerned about housing wealth which links to consumption functions. According to Muellbauer and Murphy (1997), two types of measurements are available for calculating booms and busts in housing market. They are: the ratio of housing price over income ratio; and investment return on housing. Some analysts used new econometric models to measure the causes and facts of the issues of booms and busts in measuring the second hand UK housing prices to income during the period 1954 to 1994. The calculations were based on adjusted and non adjusted indices which related to income. Muelbauer and Murphy (1997) also used three analyses by using the relative rate of return of housing price to income. They used the measurements of un-geared, average gearing and average gearing of first time potential buyers. The measurements discovered a different perspective from the previous model in developing housing market for the past 40 years. Muelbauer and Murphy also found that rate of return is an important key of housing market demand. The use of both new econometric model and rate and return models highlight the problem of unpredictability in house prices.
There are few key variables in housing market (http://www.newyorkfed.org/research/), such as housing prices and the volume of output (figure 2.3); new house prices and consumption of nondurable goods (figure 2.4); investment and output volume (figure 2.5); housing prices and consumption of durable products (figure 2.6); price and investment in housing market (figure 2.7); price and inflation rate in the housing market (figure 2.8); price and level of consumption (figure 2.9); and net equity and equity withdrawal in the housing market (figure 2.10).
Other measurements of the house price market such as the financial accelerator models (Bermanke, Gertler and Gilchrist, 1999) and the BGG model are used. The BGG model refers to a dynamic general equilibrium model which relates to the issues of macroeconomic effects. The use of the BGG model is to measure the external economic effects in the imperfect credit markets. This also includes the strengths of borrowing decisions in the money market. The dynamic of the relationship between consumption and housing prices is shown to have a positive impact on the monetary policy. The Vector Autoregression (VAR) model is used to establish a relationship between the variables. Other factors also are included including such issues as period of volumes, inflation rate, the price of petroleum oil, monetary balances and short term interest rate, etc (Aoki, Proudman and Vlieghe, 2009).
There are few housing price indices that can be readily obtained. Figure 2.11 shows the main price indices in the UK housing market (Wood, 2009).
The housing market condition is linked to the ratio between the price and income. It is because when an individual's income is increased, the possibility of purchasing a house or flat is high. With a high income is also easier to get a loan from bank. It is also believed when an individual's income is low, the possibility to buy a house/flat is low. However, if majority of peoples' incomes are low, the price to rent is relatively high.