ECO5 Ltd: Housing Price & Rent Analysis - Economic Principles & Impact

This essay provides an analysis of housing prices and rent in two areas from 2007 to 2018, considering various economic factors and government interventions. It discusses how factors like interest rates, infrastructural changes, and speculation influence housing prices. The analysis highlights the impact of government policies, such as rent control, on the accommodation market, including potential consequences like misbalanced maintenance and population density changes. Demand and supply curves are used to illustrate the effects of these factors and policies on housing prices and rent, offering insights into the complexities of the real estate market. The study concludes that understanding market conditions is crucial for property valuation and investment decisions, advocating for policies that encourage investment in the housing sector.

Running head: ANALYSIS OF HOUSING PRICE AND RENT
Analysis of Housing Price and Rent
Name of the Student:
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1ANALYSIS OF HOUSING PRICE AND RENT
Table of Contents
Introduction:...............................................................................................................................2
Discussion..................................................................................................................................2
Conclusion..................................................................................................................................4
Reference:..................................................................................................................................6

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Introduction:
The real estate, construction business, built environment sector and its growth depend
on various factors. Some of the factors are logical based on economic theories like interest
rate, demand and supply, economic growth, population growth and population density and
some are based on intangible factors like infrastructure and changes in nearby properties.
Except these all reasons, government can also control the prices of house by changing the
interest rate. Now if two areas are considered then it is possible that two different policies for
different areas, different infrastructure and different demand depending on the location can
potentially make the prices different in two locations. Here an analysis is presented
depending on the given data.
Discussion
From the given data, it is seen that the growth of the housing prices in two areas is
same from the year 2010 to 2013. The possible reason for the growth is government
intervention. Government can control the prices by changing the interest rate. A higher
interest rate can increase the cost of mortgage payments for the mortgage lenders that can
make purchasing house less attractive by effecting the affordability of purchasing a house
(Kuttner and Shim 2016). Now the government of these two areas can raise or reduce the
interest rate accordingly over these three years to influence the demand of housing which
effects the price of houses. From the year 2014 to 2018, growth of an area is opposite to the
growth rate of the other area. Simply, if there is a positive growth in the area A then negative
growth is present in the area B. The possible reason of these incidence is a mixed effect of
speculation, infrastructural change and interest rate. An infrastructural change in an area can
increase the demand for the houses as factors like new workplace, shopping malls, flyovers,
schools and hospitals contribute in the increase in demand. Projects which improves the

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connectivity of nearby locations raises the price (Gonzalez-Navarro and Quintana-Domeque
2016). Speculation about capital gains and income from renting through buying houses by the
investors influence the prices. This also can influence the house rents (Nathanson and Zwick
2018). Now in 2014, it is possible that there is a change in infrastructure which shows the
growth of prices in the area A and the demand for the houses in area A reduces the growth of
price in area B if it is possible to move from area A to area B. That means there will be a
rightward shift in the demand curve of houses in area A and a leftward shift of the demand
curve for the area B. The figure 1.a shows the rightward shift of demand curve of the houses
in area A for which the price and the quantity is rising.

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Figure 1.a: Housing market in area A
The figure 1.b shows the leftward shift of demand curve of the houses in area B for
which the price and the quantity is decreasing.
Figure 1.b: Housing market in area B
These are the one possible series of incidents where factors play their role to effect the
demand and price of the houses which looks like the graph that is shown in the above
diagrams. There are more possible series of incidents where the factors may differ or include
more factors like economic growth, inflation and population density which may give the
same result.
Now, a discussion is presented about the rent control by government that set a limit on
the rate of rent increases to house price inflation -1% per annum. The discussion includes a

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graphical presentation with a demand supply curve which shows the initial and the rent after
imposition of rent control policy by government. The below diagram shows the ceiling price
Pc which is set by the government. The equilibrium rent price is P and the quantity is Q. After
introducing the ceiling price the demand becomes Q2 and the supply becomes Q1. The excess
demand is (Q2-Q1).
Figure 2: Housing market after price control
Now it is clear that the rent of the house in the accommodation market is lower than
before for rent control policy of government. This incident will attract those people who
wanted to stay in the area but were not able to rent a house in that area. This will increase the
population of that area and the population density of the area (Öst, Söderberg and
Wilhelmsson 2014). On the other side, these rent control may result in deterioration of

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housing, number of repairs will be less and maintenance will reduce. Thus the
accommodation market will be misbalanced in terms not getting proper maintenance and
repairs. Though the sitting tenant is protected by rent control but in most of the cases they do
not receive real rental bargain because of poor painting and grudging provision of services.
The house owners will start to lose the incentive which they had in the free market. But in
opposite, to refinance the mortgage he can legally charge.
Conclusion
The real estate construction business and built environment sector is very much
unpredictable. The supply side factors that affect the price of housing are not affecting in the
short run as the supply of housing is fixed because a house is built over a period. Therefor the
demand side influence on price of houses is greater than the supply side. The factors
discussed above gives a clear view that if there is no inflation then the price of houses do not
vary too much. So when one decides to sell or buy the property it is important to analyse the
market conditions to evaluate the property valuation in the market over time. The negative
result of rent control policy is making property owners to pay an escape charge from the law
(Micheli and Schmidt 2015). The confident way to encourage the investment is to giving an
indication to the investors that housing will be free from rent control policy.

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Reference
Gonzalez-Navarro, M. and Quintana-Domeque, C., 2016. Paving streets for the poor:
Experimental analysis of infrastructure effects. Review of Economics and Statistics, 98(2),
pp.254-267.
Kuttner, K.N. and Shim, I., 2016. Can non-interest rate policies stabilize housing markets?
Evidence from a panel of 57 economies. Journal of Financial Stability, 26, pp.31-44.
Micheli, M. and Schmidt, T., 2015. Welfare effects of rent control—A comparison of
redistributive policies. Economic Modelling, 48, pp.237-247.
Nathanson, C.G. and Zwick, E., 2018. Arrested Development: Theory and Evidence of
Supply‐Side Speculation in the Housing Market. The Journal of Finance, 73(6), pp.2587-
2633.
Öst, C.E., Söderberg, B. and Wilhelmsson, M., 2014. Household allocation and spatial
distribution in a market under (“soft”) rent control. Journal of Policy Modeling, 36(2),
pp.353-372.
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