FACTORS AFFECTING GROWTH IN REAL ESTATE INVESTMENT IN KENYA ETYANG’ EDWIN MUYODI K16S/NKU/24887/2015 A RESEARCH PROJECT SUBMITTTED IN PARTIAL FULFIILMENT OF THE REQUIREMENTS FOR THE AWARD OF BACHELORS DEGREE IN ECONOMICS AND FINANCE

FACTORS AFFECTING GROWTH IN REAL ESTATE INVESTMENT IN KENYA
ETYANG’ EDWIN MUYODI
K16S/NKU/24887/2015
A RESEARCH PROJECT SUBMITTTED IN PARTIAL FULFIILMENT OF THE REQUIREMENTS FOR THE AWARD OF BACHELORS DEGREE IN ECONOMICS AND FINANCE, KENYATTA UNIVERSITY
26, July 2018
DECLARATIONI declare that this project is my original work and has not been submitted to any other university or an institution of higher learning for award of degree.

Signed………………………. …………… Date……………………………
EDWIN MUYODI
K16S/NKU/24887/2015
This research project has been submitted for examination with my approval as the Kenyatta University supervisor.

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Signed…………………………………….Date……………………………….

MR OMONDI
Lecturer
School of Economics, Kenyatta University
ACKNOWLEDGEMENTSI wish to give thanks to the Almighty God, my lecturer Mr. Omondi for his continuous follow up through this research project and my family for their support during my school work and having instilled in me the virtue of yearning to learn more.

DEDICATIONThis dissertation was done to honor my parents and siblings, who are behind my success up to now in terms of where I have reached especially education wise due to their constant reminder on the need to have education.
ABSTRACTReal Estate investment represents a significant portion of people’s wealth and this is especially true for many real estate investors in Kenya. Real Estate investment plays crucial role in providing employment opportunities, offering shelter to households, enhancing income distribution and alleviating poverty. However, the investment in Kenya continues to fail to fulfill this fundamental role due to a number of unique factors that affect the sector. The study investigated factors such as GDP Growth, the influence of interest rate, inflation rates and population growth. The study adopted both quantitative and descriptive research design to obtain information especially true for many real estate investors in Kenya. The study therefore, investigated the contribution on the current status of the phenomenon. The population in this study was real estate investors while the target population included private and public property investors. Data for analysis was based on the real estate and renting businesses as sourced from the various Economic Surveys and Kenya Statistical Abstracts Issues. The data obtained was analyzed by use of the Statistical Package for Social Sciences (SPSS) to obtain descriptive statistics and a regression model. From the results the contribution of the factors affecting real estate growth as measured by Pearson correlation coefficients indicated that GDP took the highest share with a value of 83%followed by inflation growth at 78% while interest rate came third with value of 75%.Population growth contributed the least to the growth in real estate investment with a value of 29%. Therefore the study hypothesis that GDP is the most significant contributor to the growth in real estate was supported by the data. In addition GDP growth, interest rate variation and growth in inflation were found to be statistically significant determinant of real estate growth. A summary of the regression results showed that the variables considered could explain up to about70% of variations in the investment growth. The study recommended that Policy measures geared toward improving the economic growth and curbing rising inflation rates and interest rates should be undertaken as they increase the investment levels. Finally the study recommended future research on the impact of population growth on real estate investment
ACRONYMS
GDP- Gross Domestic Product
REI- Real Estate Investment
REIT- Real Estate Investment Trust
SPSS- Statistical Package for Social Sciences
CHAPTER ONEINTRODUCTIONBackground of the studyReal estate investment plays crucial role in providing employment opportunities, offering shelter to households, enhancing income distribution and alleviating poverty. However, the real estate industry in Kenya continues to fail to fulfill this fundamental role due to a number of unique factors that affect investment in the sector. In the recent past, Kenya has witnessed an upsurge in real estate investment. This has been driven by a number of factors notably the quest for Kenyans to own homes, rural urban migration, increased Diaspora remittances among others. As a result, property prices in the urban areas have taken an upward trend. The expansion of Mombasa road and the construction of Thika super highway have also contributed to the rise of property prices in the adjacent areas. It’s therefore important to assess the factors that contribute to investment growth so as to sustain it’s the growth in future. Real estate is property consisting of land and the building on it along with its natural resources such as crops minerals or water immovable property of its nature an interest vested thus an item of real property building or housing in general. Real estate investing involves the purchase, ownership, management, rental land or sale of real estate for profit. Kenyan real estate property covers all property categories including single and multi-family residential dwellings, commercial and agricultural land, office space, go-dawns and warehouses, retail outlets and shopping complexes (Masika, 2010). Real estate is an asset form with limited liquidity relative to other investment, it is also capital intensive (although capital may be gained through mortgage leverage) and is highly cash flow dependent. If the factors affecting the growth in the investment are not well understood and managed by an investor, real estate becomes a risky investment. The primary cause of investment failure for real estate is that the investor goes into negative cash flow for a period of time that is not sustainable, often forcing them to resell the property at a loss or go into insolvency. A similar practice known as flipping is another reason for failure as the nature of the investment is often associated with short term profit with less effort. Real estate markets in most countries are not as organized or efficient as market for other more liquid investment instruments. The individual’s properties are unique to themselves and not directly interchangeable, which presents a major challenge to an investor seeking to evaluate prices and investment opportunities. For this reason, locating properties in which to invest can involve substantial work and competition among investors and to purchase individual properties may be highly variable depending on knowledge of availability. Information asymmetries are commonplace in real estate markets. This however, increases transaction risks, but also provide many opportunities for investors to obtain properties at bargain prices. Real estate investors typically use a variety of appraisal techniques to determine the value of properties prior to purchase. Investment in real estate is undertaken for its ability to provide returns inform of capital, income and intangible benefits (Baum ; Crosby 1988). However returns in commercial real estate are maximized when there is full occupancy, prompt and total rent collection, full market rent, good physical condition of building; minimal irrecoverable outgoings and low rate of tenant turn over. Studies by Ziening ; McIntosh (1999) and Tonto, Wheaton ; Southard (1998) have shown that the greater volatility in return in commercial real estate is not an appraisal problem but a structural problem of the property markets and real estate property as an investment vehicle. The most typical sources of investment properties include: market listing (through multiple listing service or commercial information exchange), real estate agents, wholesale (such as banks real-estate owned department and public agencies), public auction (foreclosure sales, estate sales), and private sales.

1.2 Statement of the ProblemReal Estate comprises lands plus anything permanently fixed to it, including buildings and other items attached to the structure. Examples of real estate include undeveloped land, houses, townhomes, office, building, retails store and factories (Brown and Matysiak, 2000). According to Syagga (1987) the principal types of real estate property includes-: rural land use (which consists of farmland, forestry and mineral land), urban land which consists of (commercial, industrial and residential properties) and special type of property such as (petrol stations, recreational facilities, hotels and restaurants, halls and places of assembly and institutional property). The real estate market and industry covers land and improvements, their selling and rental prices, the economic rent of land and returns on buildings and other improvements, and the construction industry. The investment represents a significant portion of people’s wealth, and this is especially true for many real estate investors in Kenya. However commercial real estate in Kenya has been faced with shrinking occupation demand and there exists disparities between expected and actual income which may be either positive or negative (Murigu 2005). Real estate prices in Kenya has doubled, even tripled in the past few years (Majtenyi, 2010) and the government wants to know the cause. Demand for housing units continues to outstrip the supply (Masika, 2010). The size and scale of the real estate market makes it an attractive and lucrative sector for many investors. Uri, E. & Frank No that,(2002) in a study found that the population of Kenya has steadily increased, resulting in an urban population in Nairobi of a record of 3 million people, whereby all these people need shelter, hence the real estate industry is tremendously doing well and contributing to the economy’s growth. Real estate investments and prices are good measures for reflecting expected real estate demand, and serve as good predictors of economic growth (KnightFrank, 2011). A survey conducted by Hass Consultants in association with CFC Stanbic bank in the year 2010 revealed that the Kenyan real estate sector has been vibrant for the past decade between the years 2000 to 2010. For instance the report also indicated that capital gains from Kenyan properties far outstrip gains from US and UK properties. This has eventually made the Kenya real estate market to be the winner in the international property investment amidst the indebtedness in the Western Countries (Mwithig, 2010).According to a report by the National Housing Corporation (NHC), the Vision 2030 estimates that the country requires 200,000 new units of housing but only 35,000 units have been produced to date. That means we have a deficit of 165,000 housing units. Similarly, a report from the Kenya National Bureau of Statistics (KNBS) indicates that real estate investment has contributed lot to the growth of Kenya’s Gross Domestic Product. For instance data from Kenya National Bureau of Statistics report (2012) shows that, in 2008, real estate contributed 107, 323, 000shillings to the country’s GDP. In the subsequent year, 2009, the value of GDP attributable to real estate reduced slightly to 116,657,000 Kenyan Shillings. In addition the value of GDP further rose in 2010 to 123,173,000 shillings and consequently the contribution to GDP from real estate rose further in 2011 to 134, 746, 000 Kenyan shillings. Real estate and renting business services play a crucial role in the Kenyan economy (statistical abstract 2011). For instance the investment grew at 3.5% in 2007 and rose slightly to 3.7% in 2008.However, the growth declined sharply to 3.0% in 2009 due reduction in capital investment and the poor performance of the economy as a result of the post-election violence that led to destruction of property and in the 2007 General elections. The growth picked up in the preceding years at 3.2% and 3.6% respectively in 2010 and 2011 respectively as investment climate became conducive and by the of the end of the third quarter of 2012 the investment was growing at 3.8% depicting an increasing trend. There has been a great appreciation of property prices and volatility across the different property markets in Kenya since the year 2006.According to Hass property consultants, in the first property index in Kenya, the prices for high end residential properties has doubled between 2005 and 2009 (Hass property index, 2009). The current rental yields that are the return on capital tied up in property is however much lower than mortgage interest. The Hass consultant property index data for the first quarter in 2011 indicated that rental yield are down to 5.62 per cent per from a high of 7.3 percent per year in 2007. The Hass survey further revealed that property prices have risen to 55 per cent since the 2007 while rental yields have appreciated with only 18 per cent. The main concern is that real estate contribution to the economy of Kenya (as measured in relation to the economic growth) has faced a declining trend for the past years. For instance in 2008, it contributed to 5.1% of total GDP, and in 2009 it reduced to 4.9% of GDP. Subsequently it slightly fell to 4.8% in 2010 and further declined to 4.5% in 2011. There is need therefore establish and assess the factors that contribute to the growth of the investment so as to sustain the investment growth in future.

1.3 Research Questions
(i) What is the impact of GDP on the growth in Real Estate Investment?
(ii) What is the contribution of interest rates to the growth in Real Estate Investment?
(iii) To what extend do changes in inflation rate affect the growth in Real Estate Investment?
(iv)What is the impact of population growth on Real Estate Investment?
1.4 Objectives of the study(i) To examine the impact of GDP on the growth in real estate investment in Kenya.

(ii) To determine the contribution of interest rates on the growth in real estate investment in
Kenya.

(iii)To determine the extent to which inflation rates affect the growth in real estate investment.

(iv)To examine the impact of population growth on the growth in real estate investment.1.5 Significance of the studyThe results and findings from this study will form a basis for policy formulations on ways of controlling for the determinants of real estate so as to sustain the investment growth in future.

1.6 Scope of the studyThe study investigated the factor affecting growth in real estate in Kenya with emphasis on the assessment of the various contributions of factors such as GDP growth, inflation rates, population growth rates and rate of interest. The study area involved private and public developers in real estate property. Data for the study was on time series covering real estate renting business.
CHAPTER TWOLITERATURE REVIEW2.0 IntroductionThis chapter presents a critical review of the research work that was done by various scholars in the field of property management and more specifically the Real Estate investment. For instance the increasing interest in the real estate investment in recent years has naturally caught the interest of many academicians. Many people tend to think real estate properties are only purchased for appreciation (increase in value) or for the production of income. However, any purchase of interest in real property can be regarded as being for investment purposes. For instance there is the case of one purchasing an interest in property, which is to be left to someone to provide the benefit of income and there is the purchase for occupation that is also an investment; the benefit being the annual value of occupation. A businessman for instance, may choose whether to buy property for own business or to rent to someone and invest the capital elsewhere. Review of literature shows that the growth in real estate investment is influenced by factors mainly related to the performance of the economy.

2.1 Performance of Real Estate
Real Estate investors have long been aware of the challenges of translating the returns of property investment into reliable time- series data (Fisher ; Boltzmann, 2005). This has been overcome by developing statistical risk and return inputs to allocation models through the construction of indices that reflect broad trends in diversified portfolio of investable properties. These include: – time weighted rate of return, time, internal rate of return and simulation procedure. Studies by Hammers ; Chen, (2005) measure real estate performance by analyzing return on asset. Similarly, Fisher, (2005), using the internal rate of return (IRR) to stimulated portfolios comprised of commercial properties, U.S stocks and U.S. bonds. Ooi ; Liow, (2004) using systematic risk incorporated in the traditional Capital Asset Pricing Model (CAPM) to explain real estate returns. For instance Fisher, (2005), observed that stock and bond portions of the portfolio are re-balanced to accommodate the positive and negative cash flows required by real estate investing. This simulated IRR approach helps to examine the cross sectional distribution of real estate returns over the time period. He further argued that inflation protection is one of the main reasons that institutions invest in real estate. In addition, Kohnstamm (1995) argued that apart from risk, inflation and rate of return as measures of real estate performance, rental income has been the most preferred measure by investors.

2.1.1 Macro economic variablesCase, Goetzmann, and Rouwenhorst (2000) explored returns in global property markets, and found the returns heavily related to fundamental economic variables such as GDP, inflation and economic growth, while Ling and Naranjo (1997, 1998) identified growth in consumption, real interest rate, the term structure of interest rate, and unexpected inflation as systematic determinants of real estate returns. In addition studies by Hekman (1985) highlighted GDP as being the most important influence on return levels, whereas unemployment rate was found notto have any significant impact. The insignificance of employment was backed up by Dobson and Goddard (1992) findings, coinciding with the De Wit and Van Djik (2007) conclusions. The most extensive research of real estate and the macro economy is in terms of real estate’s hedging capabilities against inflation. Hartzell et al. (1987), Wurtzebach et al. (1991) and Bond and
Seiler (1998) proved that real estate provides an inflation hedge across property sectors, and the findings was confirmed by Liu, Hartzell, and Hoesli (1997) as well as Huang and Hudson-Wilson (2007) that found United States real estate market having good hedging abilities.

2.1.2 Economic ActivityFrom previous found review it has been that the growth in real estate in a country is among others depended on the changes in economic activity and prosperity of a region or country. According to the model of DiPascal and Wheaton (1992), a productive economy does positively affect the demand for real estate assets. Similarly, Chin, Dent and Roberts (2006) conclude from survey data that a sound economic structure and an expected strong and stable economy are perceived to be the most significant factors in the ability of a region to attract foreign real estate investments. Besides, Hoskins, Higgins and Cardew (2004) found that GDP growth, inflation and unemployment show significant relations with composite property returns. In addition other researchers such as Chen and Hobbs (2003) found that the size of a country’s economy positively affects investment activity, as larger economies are usually more capable of withstanding external economic turmoil and are therefore more stable than smaller economies. Results of studies from Van Doorn, (2003) revealed that GDP per capita is commonly used in strategic real estate asset allocation for the determination of a country’s economic level of development. Other researchers such as Connor and Liang (2000) argued that over the long term, the impact of technology on real estate has been overwhelmingly positive as advancement in technology affect positively the investment climate. Similarly technological advancement have resulted to increased productivity and wealth, demand for all types of real estate has also increased. The latter even analyzed the property sectors individually and found that office and residential by far outperformed retail and industrial regarding inflation hedge. It is however noticeable that Stevenson et al (1997) found no signs of selection ability among investment managers, while there is evidence of superior market timing ability i.e. managers are capable of actively using the macroeconomic environment in order to achieve Macroeconomic Determinants of Real Estate Returns superior returns.

2.2 Rental IncomeRental income is a return gained after using a property for a particular period of times for example a house, land, building etc. In Korea, the most popular type of rental income is called “chonsei.” Under a chonsei arrangement, the tenant leaves a lump-sum deposit to the landlord at the beginning of the lease contract in lieu of monthly rents (Kyung –Hwan, 1990). At the end of lease, the entire deposit is returned to the tenant. The landlord invests the deposit and keeps the return for the investment. Chonsei is an ingenious but financially inefficient system. It essentially forces the landlords to serve as a financial intermediary at their own risk, even though they may not have the required skills or information. Tenants may not be able to assemble a large amount of money to make the deposit for the dwelling unit they desitry5dtgere and settle for a smaller unit, i.e., lower their housing consumption. Rental income is usually determined by a number of variables over time for example the Gross Domestic Product (GDP), output, Employment for financial and business services, unemployment, interest rates and operating expenses in office space (Matysiak and Tsolacos, 2003). In retail sector, expenditure, retail sales and the GDP seem to be the most successful demand side indicators. In industrial market, the GDP and manufacturing output seem to be the most significant variables. In general demand and supply and the economic variables will determine the rental income in real estate.

2.3 Real Estate Investment OpportunitiesHan (1996) concludes from his survey that real estate investment opportunities, demographic attributes, and the market structure are important selection criteria for investment decisions. The accessibility of institutional real estate via different ownership ratios is a critical factor in real estate investment due to the close relationship between market entry probability, liquidity risk, and transparency of markets. Similarly, Ling and Gordon (2003) study estimated the availability of higher quality not owner-occupied commercial real estate in a theoretical model. Kurzrock (2007) finds via cross-sectional regression that a high degree of agglomeration has a positive impact on property performance. Obviously, accelerating urbanization, which determines the structure, potential and quality of the real estate environment, plays an important investment decision. This was especially valid for the US, where urban areas are spreading across major regions, pushing upland and building values, and making real estate assets increasingly valuable.

Lynn (2007) notes, that improvement in communication and transportation infrastructure facilitates the migration to cities and drives the pace of urbanization, which will support new development. Furthermore, the financial and business service sectors reflect a growing level of sophistication in the service economy and thus, the demand for commercial real estate.

2.4 Tests for determinants of real estate bubblesMost studies on determinants of real estate bubbles focus on demand side factors namely credit growth and GDP growth (Collyns and Senhadji, 2003). Capital inflows into a country inevitably channel itself into asset markets through the formation of easy credit and are suspected to contribute significantly to real estate bubbles. Wong (2001) documented growth in Thailand’s housing market, which was fuelled in the 1990s by the liberalization of capital inflows as a result of the passage of the Bangkok International Banking Facilities in 1992. This provided opportunities for domestic financial institutions to borrow from foreign sources at low rates and in turn disseminate the money to local housing developers. This led to two types of bubbles in Thailand namely a real estate bubble which led to a run up of real estate prices and an overcapacity bubble, which resulted in faster completion of housing and commercial projects than what the real estate market could absorb. The final outcome led to the collapse of the real estate market in Thailand just before the 1997 Asian financial crises. Rents, which are typically seen as demand driven, depend on real GDP (which acts as a proxy for aggregates level of income per capita and population size). Rising real GDP will increase the wealth of the population as a whole contributing to an increase in discretionary incomes. This income can be channeled into asset markets, namely real estate. The real estate sector which is governed by long construction lags thus will see rising real estate prices and also rentals. There is also some basis that the stock market also impacts the real estate market. Bardhan, Datta, Edelstein, and Lum (2003) have documented significant positive impact of stock equity wealth on the number of new private housing units in Singapore. This suggests that an increase in the stock market would increase the wealth of investors who eventually cash out and reinvest their profits into real estate. Thus, the wealth effect in the stock market spills over to the real estate market. Interest rates play an integral part in real estate as most purchases of real estate property tend to be acquired on a mortgage basis. In a declining interest rate environment, the cost of servicing a loan becomes smaller. This typically allows households to take a bigger mortgage within their current income budgetary constraints. This ultimately boosts the demand for and price of residential real estate. Using vector auto regression methodology, Tsatsaronis and Zhu (2004) found that interest rates especially short term interest rates explain almost 10.8 per cent of the variation in house prices. It is postulated from their model that a negative one percentage point change in the real short-term interest rate leads to an increase of 1.2 per cent in house prices over two years. They also found that countries which use predominantly floating-mortgage rates demonstrate higher impact of short term rates on house prices.

2.5 Determinants of real estate valuesPrevious studies on real-estate values and neighborhood effects utilize aggregate census data where the unit of observation is a census tract. The quality controls on the units are also aggregative and include such variables as percentage of units in a tract classified as dilapidated and/or without private bath, percentage of houses which were more than twenty years old and median number of rooms per dwelling. Among the studies utilizing census data is the work of Richard Muth (1967, 1970) on urban structure, the relationship between low-quality housing and poverty and between housing prices and race. Also Ridker and Henning (1967) use these data in analyzing the effects of air pollution on property values. In addition Oates (1969) in his study of influence of public services and taxes used average property values for a sample of New Jersey cities. There have also been several studies using micro (disaggregative) data in which the unit of observation is a single transaction. For example, Bailey (1966) studied the effects of racial composition and population density on housing prices in Chicago. Pendleton (1962) attempted to measure the value of accessibility to job opportunities and to the central business district in Washington, D. C. Kain and Quigley (1970) examined the effects of a variety of neighborhood quality indices on housing prices in St. Louis, and Lapham (1971) used data from Dallas to test the hypothesis of racial discrimination in housing.

2.5.1 Real estate prices
Most studies point to the increase in credit growth as one of the main determinants of the run up in real estate prices. Koh et al. (2005) using an option-based model of financial intermediaries found that if the value of the underlying asset falls below the outstanding amount of a loan, the borrower may simply default on the loan putting the asset into the hands of the financial institution. This may cause the financial intermediaries to hold excessive amounts of unwanted real estate which in a bear market can only be disposed at prices which were dramatically lower than the amount it was originally collateralized for. The banking system is the dominant financial system in most East Asian countries where the equity and bond markets are fairly underdeveloped (Collyns and Senhadji, 2003). Another possible determinant of real estate price dynamics is real Gross Domestic Product (GDP) which captures both the aggregate level of income per capita and population size (Ho and Cuervo, 1999). An increase in real GDP would increase the income of the population in the economy resulting in increased demand for real estate through higher prices of primary property and higher rentals. Real interest rate is also another possible important determinant. A reduction in real interest rates can increase the prices of real estate as it reduces the cost of borrowing. Reflecting these developments, outstanding mortgages as a share of GDP has risen dramatically, particularly among smaller European countries (IMF, 2003). In quite a few European Union (EU) countries the (negative) correlations between real housing prices and real interest rates have been especially high.

2.5.2 Pricing modelIn most empirical studies, Price Model is used to identify and measure the effect of environmental valuables and building characteristics on property values. This modeling approach assumes that the monetary value of a dwelling unit depends on the attributes a particular house or apartment may possess. For instance, the market price of a dwelling may reflect its physical size and environmental characteristics, such as the number of rooms, age and location. Plaut (2003) alludes that although the price method is, undoubtedly, the most commonly used research tool for investigating the negative and positive effects of neighborhood, amenities and building characteristics on property values, some underlying assumptions of this method may, nevertheless, be questioned. According to Rosiers (2002) for instance, the hedonic price approach assumes the existence of direct links between environmental factors and building characteristics, on the one hand, and property values, on the other. However, these factors likely correlate indirectly, through the investment decisions of property owners.

2.6 Mortgage Interest RatesThis to a great extent will determine affordability alongside the maturity. A study from Uganda revealed that Interest rates range between 16% – 23% depending on the purpose of the mortgage
(Kibirige, 2006). Usually owner occupier mortgages take the lower rate and it increases as one tends towards commercial mortgages. These rates are generally high and are attributable to the lack of long term local funding. Similar study in Egypt, on mortgage lending rate revealed that the mortgage rate equals to 14% with a margin of 4% over the prime lending rate (Hassanein and Barkouky, 2008). This leaves mortgage companies with only 1.5% which will be further decreased when attempting to securitize the mortgage loan and provide other guarantees.

2.7 Drivers of house prices.According to Debelle (2004), investigation relates to the importance of inflation as a driver of housing prices. On average, across countries, inflation accounts for more than half of the total variation in house prices. In the short run, the size of the impact is even larger. Debelle alludes that its contribution nears 90% of the total price variation in the one-quarter horizon and drops to about two thirds over the one-year horizon. This strong influence of inflation is more important when one considers that house prices are measured in real terms. There are two potential explanations for this finding. The first relates to the dual function of residential real estate as consumption good and investment vehicle. As such, it is often used by households as the main hedge against the risk that inflation might erode their wealth. The fact that the purchase of property is typically financed with nominal debt makes it more attractive in this respect. A high degree of inflation persistence also suggests that the effects of innovations in inflation on house prices are likely to be felt over longer horizons. Higher uncertainty levels about future expected returns on investments in bonds and equities associated with high inflation also contribute to the attractiveness of real estate as a vehicle for long-term savings. The second explanation is linked to the impact of inflation on the cost of mortgage financing and generally suggests that higher inflation would have a negative impact on house prices. If financing decisions are more sensitive to the nominal yield curve than to real rates, one would expect housing demand, and thus real house prices, to respond to changes in inflation and to expected inflation. In addition, inflation may also be a proxy for the prevailing financing conditions, which have an impact on the demand for real estate. High inflation and high nominal interest rates backload the repayment of the mortgage principal and increase the real value of repayment in the early part of the repayment period of the loan, thus dampening the demand for housing. In Kenya, the housing sector has been characterized by inadequacy of affordable and descent housing, low level of urban home ownership, extensive and inappropriate dwelling units, including slums and squatter settlements.

2.8 The Role of the Private Sector
The largest producer of housing units in the Private sector housing is defined as any production which is not connected at all with the actions of the state, neither directly constructed by the state nor financially sponsored by the state, where production is not expected to have a social element (Golland, 1996). Ambrose and Barlow (1987) have argued that three factors are important in influencing the level of new house building. These are direct capital investment by the state for public housing, state support for production and consumption and changes in the profitability of house builders in the private sector. The private sector can play an important role in housing provision, provided that the state offers sufficient and appropriate incentives to the sector (Mitullah, 2003). In Kenya, the private sector, both formal and informal, remains country. Initiatives by the private sector can be both large-scale and deep in impact, contrary to the government initiatives which may be large-scale but usually limited in impact (Otiso, 2003). The clear motivation that underlies the private sector is profit (or potential profitability) with profit-maximizing options being, in the context of housing, producing and selling more of the product; reducing the cost of production through lower raw material and wage costs (cost per unit or quantity) and finally, increasing the price of the product or service (Hancock, 1998). The private sector is capable of providing living needs to large segments of the urban community if they operate within a well-conceived competitive environment where there is a possibility of charging consumers and making a profit, absence of daunting obstacles such as technology andscale of investment and the presence of competent governments with the capacity to enforce standards, contract fulfillment and service provision (Otiso, 2003). Ball (1996) suggests that the trigger of development activity is an analysis of market opportunities by developers who see demand for new housing, anticipate adequate return on investment, gear their resources towards purchase of land and housing production and then sell these housing units with a view to maximizing profits. Profitability in housing is advocated to be based on three variables: House prices, land prices and building costs, where: Profit = House Prices – {Land Prices + Building Costs} (Golland, 1996). Macoloo (1994) defines the key components of housing to be land, finance, building materials and construction technologies, these relating to the costs in the profit model above. In a survey of developers, Thalmann (2006) however purports that few market developers actively monitor the market for business and profit opportunities but instead respond to market triggers, such as availability of land. As such, the supply of housing may not respond only to market signals and incentives.

Figure 2.1: Conceptual FrameworkINFLATION RATE
DEMOGRAPHICS
INVESTMENT RATE OF INTEREST
GROWTH IN REAL ESTATE INVESTMENT
INDEPENDENT VARIABLEPREDICTOR VARIABLEDEPENDENT VARIABLE
GROSS DOMESTIC PRODUCT GROWTH

ININ
Factors Affecting Growth in Real Estate Investment in Kenya
Factors that Influence Real Estate real estate include demographic factors, rate of interest, inflation rate, performance of the economy among others. Demographics are the data that describes the composition of a population, such as age, race, gender, income, migration patterns and population growth. These statistics are an often overlooked but are significant factors that affect how real estate is priced and what types of properties are in demand. Major shifts in the demographics of a nation can have a large impact on real estate trends for several decades. There are numerous ways this type of demographic shift can affect the real estate market, but for an investor, some key questions to ask might be: i) how would this affect the demand for second homes in popular vacation areas as more people start to retire?
ii) How would this affect the demand for larger homes if incomes are smaller and the children have all moved out? These and other questions can help investors narrow down the type and location of potentially desirable real estate investments long before the trend has started. Interest rates also have a major impact on the real estate markets. Changes in interest rates can greatly influence a person’s ability to purchase a residential property. That is because as the interest rates fall, the cost to obtain a mortgage to buy a home decreases, which creates a higher demand for real estate, which pushes prices up. Conversely, as interest rates rise, the cost to obtain a mortgage increases, thus lowering demand and prices of real estate. However, when looking at the impact of interest rates on an equity investment such as a real estate investment trust (REIT), rather than on residential real estate, the relationship can be thought of as similar to a bond’s relationship with interest rates. When interest rates decline, the value of a bond goes up because its coupon rate becomes more desirable, and when interest rates increase, the value of bonds decrease. Similarly, when the interest rate decreases in the market, REITs’ high yields become more attractive and their value goes up. When interest rates increase, the yield on an REIT becomes less attractive and it pushes their value down. Another key factor that affects the value of real estate is the overall health of the economy. This is generally measured by economic indicators such as the Gross Domestic Product, employment data, manufacturing activity, the prices of goods, etc. Broadly speaking, when the economy is sluggish, so is real estate. However, the cyclicality of the economy can have varying effects on different types of real estate. For example, if an REIT has a larger percentage of its investments in hotels, they would typically be more affected by an economic downturn than an REIT that had invested in office buildings. Hotels are a form of property that is very sensitive to economic activity due to the type of lease structure inherent in the business. Renting a hotel room can be thought of as a form of short-term lease that can be easily avoided by hotel customers should the economy be doing poorly. On the other hand, office tenants generally have longer-term leases that can’t be changed in the middle of an economic downturn. Thus, although you should be aware of the part of the cycle the economy is in, you should also be cognizant of the real estate property’s sensitivity to the economic cycle.

2.9 Overview of the literatureFrom the foregoing literature it quite evident that the investment in real estate in Kenya is influenced by many factors most of which are based on the economic performance of the country. For instance a number studies revealed that the investment growth depends on the economic activity prosperity. It is argued that a productive economy positively affects the demand for real estate. Besides the growth in the gross domestic product, growth in inflation rate and unemployment significantly affect the growth in real estate. The size of the country’s economy is perceived to affect the investment growth in that large economy attracts the investment faster as compared to small economies. In addition other research work have found that the mortgage interest rate, term structure of interest rate, the rate of return and rental income determine the affordability, as well as the growth in consumption and determined the level of the investment growth. From the review it is difficult to predict whether the real estate market activity in a country with a high investor protection level is more affected by the liquidity of the national stock market or by regulatory limitations. Similarly, the influence of some factors such as socio-cultural and political instabilities or legal issues cannot be quantified as they are qualitative in nature and can only be captured in terms of proxies. This imposes a challenge to determine possible proxies for the drivers of real estate investment activity, and likewise aim to keep the country coverage at a maximum. Studies by Ambrose and Barlow (1987) further identified three factors as important in influencing the level of new house building namely; direct capital investment by the state for public housing, state support for production and consumption and changes in the profitability of house builders in the private sector. In addition, Golland 1996 also shows that private investors are motivated to invest in house development due to the profit realized. For instance he argues that Profitability in housing is based on three variables: House prices, land prices and building costs, where: Profit = House Prices – {Land Prices + Building Costs}. In addition Macoloo (1994) defines the key components of housing to be land, finance, building materials and construction technologies, these relating to the costs in the profit model above. From the above review it is my view that the major factors that affect the investment growth include the gross domestic product, the rate of interest, and the inflation rate. However there is need for empirical test through data analysis to ascertain their individual contribution and their statistical significance.

CHAPTER THREE
RESEARCH METHODOLOGY3.0 IntroductionResearch methodology is a way to systematically solve the research problem. It indicates the various steps that are generally adopted by a researcher in studying his research problem along with the logic behind them (Kothari, 2004). The aim of the chapter is therefore to provide arguments for the approaches that the researcher adopted in gathering and in the treatment of the data in order to answer the research questions and objectives. In this regard, this chapter discusses the following aspects: the research design, the sampling strategies, the data collection process, the instruments used for data gathering, as well as, data analysis methods which helps in coming up with a meaningful conclusion.

3.1 Sample designThe sample comprised of real estate and renting businesses where data for fifteen most recent years was used. Data for annual time series on variables namely; interest rate, inflation rate, population growth and GDP obtained for the years between 1998 to year 2012 were used for analysis.

Population
Mugenda ; Mugenda (2003) described population as the entire group of individuals or items under consideration in any field of inquiry and have a common attribute. The population in this study was the real estate developers while the target population included private and public property developers. Data for analysis was based on the real estate and renting businesses as sourced from the various Kenya Statistical Abstracts Issues.

3.2 Research DesignResearch design refers to the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in the procedure
(Babbie, 2002).A quantitative research design was deemed the most appropriate for the analysis of the determinants of real estate investment for it allowed quantification of the influences that each independent variable had on real estate investment (dependent variable). This study adopted both quantitative and descriptive type of research design where stratified sampling technique was used.

3.3 Data Collection MethodsThe method of data collection used was secondary methods. Secondary data was obtained from Government of Kenya publications such as the Annual Budget and Financial estimates, Central Bank of Kenya Annual report, Kenya Economic Survey and statistical abstracts by the Ministry of Planning national development and vision 2030, journals, published and unpublished research work, dissertations among others.

3.4 Data AnalysisData analysis was done using Computer software, Microsoft excel and SPSS. The data collected was edited for accuracy, consistency and completeness. The data was then coded and cross –tabulated to enable the responses to be statistically analyzed. Descriptive and inferential statistics were used to analyze data by way of means, mode, median,(measures of central tendency) and standard deviation, variances, range (Measures of dispersion). The data collected was then presented in form of tables, charts and graphs. The research made use of the Statistical Package for the Social Sciences (SPSS) to estimate the result of the regression/correlation between the variables. Multivariate correlation and regression analysis was used to evaluate the degree of relationship among the variables. Multiple regressions were used to analyze the relationship between the independent and dependent variables to predict the score of the dependent variable from the independent variable. The research model to be estimated was an econometric model, multiple regression model of the form;
RE = f (GDP, int, infl, Pop)
Where RE = Real estate investment (dependent variable)
GDP = Gross domestic product (independent variable)
Int = Rate of interest (independent variable)
Infl= Rate of inflation (independent variable)
Pop=Population growth rate (independent variable)
CHAPTER FOURDATA ANALYSIS AND INTERPRATATION4.0 IntroductionIn this chapter the major focus was on bringing out the relationship between the variables outlined in the conceptual framework and the project therefore used the means of descriptive analysis in showing how the main dependent variable which is the growth of real estate investment in Kenya is being affected by either interest rates, demographic factors and inflation rates.

Table 4.1: Changes in the growth of real estate investment between 2001-2016YEAR INVESTMENT MADE
2001 48,600
2002 50,200
2003 55,071
2004 57,091
2005 58,667
2006 60,452
2007 61,864
2008 63,740
2009 65,882
2010 68,447
2011 70,860
2012 73,461
2013 75,674
2014 78,089
2015 80,606
2016 85,171
Source: Statistical Abstract, Kenya National Bureau of statistics (2012)
The country therefore has made a significant growth with regards to investment in real estate as shown in the above table which depicts how various sectors have emerged in the nation and as such the trend can be predicted as an upward coming the following years where population growth is also on its peak. This trend can be demonstrated using the figure below

Figure 4.1: Growth pattern in real estate in Kenya between 2001-2016
Due to the uncertainty of the general elections it can be observed that few individuals will be the only ones willing to invest in the real estate despite of the unforeseen expectations after the elections have been conducted and as a result the influence of this tension would be seen in the low investment made in the real estate sector in Kenya.. The economy is on a recovery path recording a 2.9% growth in 2007 close to the projection in 2006 (Economic Survey 2007). Various sectors in Kenya reported different moderate growth rates between the years mentioned above where Agriculture had1.5%, Manufacturing 1.4%, Building and construction 2.2% while Finance and real estate business had 3.0%. The GDP growth was estimated to have risen from 2.9% in 2007 to 4.9% in 2008 while according to the projections made by the Economic survey in 2009 Real GDP grew by 5.9% in 2009 from 5.1% in 2008. Apart from other negative shocks including the general elections which affected the growth of this sector there are a number of positive factors which made this sector grow up to now and these include; increased in the availability of credit facilities, investment in the infrastructural facility, remittance made by those living outside the country and the rapid economic growth that Kenya is continuing to have. With regards to government support and improvement in various sectors including the tourism sector the country had an economic growth which stood at 2.6% in 2013. Between the years of 2012 and 2013 various major sectors made a massive improvement in terms of growth as following;
Hotels and restaurants 36.5% to 42.8%
Financial intermediaries 2.8% to 5.6%
Transport and communication 3.5% to 7.4%
In 2010 the interest on a 91 day treasury bill was at 5.83%, while the inter-bank interest rates was at 6.34% and the commercial bank lending rate was at 13.77%. Lending rates in Kenya were at a high levels and there this became a major hindrance in the growth of the real estate investment. In recent years the expansion of credit facilities in Kenya has had a major impact on the growth of both the small and micro enterprises who have benefited from these expansive step which has provided them with a platform and a source of capital to be able to expand their daily activities with regards to productivity in the country and as such becoming one of the key drivers of the economy. The lending rates have always had a negative relation with the ability of one to be able to borrow from any financial institutions and therefore as the lending rates stood at 10.92% in 2004, individuals were unable to borrow from financial institutions which meant that the level of investment in the real estate sector declined and furthermore people were unable to rent homes because of the un affordability of capital being provided by the banking sectors in Kenya. Another key influence on the growth of the real estate sector is the massive growth in terms of the population levels in the country which has put pressure on the available stock of houses in the country and as such has resulted in more houses to be built due to the increase in demand from the growth of the population this therefore shows the positive relationship that exists between the growth in real estate investment and the population within a certain demographic region in Kenya whose demand has gone up. Harsh weather conditions have also been associated with decrease in economic growth especially for a country like Kenya whose major income arises out of agricultural products and therefore when this period occurs the investment to be made is always low due to the harsh economic conditions that trigger less availability of capital funds.

Table 4.2: Trend in GDP growth rate in Kenya between 2004-2014
Year Rate of growth
2004 0.5
2005 5.6
2006 0.4
2007 3.9
2008 6.1
2009 6.9
2010 7.3
2011 8
2012 1.8
2013 3.6
2014 6.8
(Source Economic survey 2014)

Figure 4.2: Trend in GDP between 2004 to 2014 8
7
6
5
4
3
2
1
0
YEAR 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

(Source Economic survey 2014)
4.2 DATA ANALYSISData analysis involved the estimation of results obtained and determination of the correlation which exists in the obtained data using F-test, ANOVA and the Linear regression model which puts both the dependent and independent variables on check to know the key relationship between them and the extent to which one affects the other for better results where assumptions were also put into use to evaluate the appropriate results.

4.2.1 Model EstimationThe multiple regression model used was in the following nature;
Y= ?+b1X1+b2X2+b3X3+b4X4+µ
Where Y = the dependent variable
?=the autonomous variable
b1, b2, b3, b4= regression coefficient under estimation
µ=the error term
Table 4.4: CoefficientsModel 1 Unstandard coefficient Standard coefficient
B Standard Error B t Sig
Constant 14.68 0.056 7.858 0.058
GDP growth rate 3.165 0.042 6.248 5.962 0.042
Interest rate -1.203 0.095 4.214 -2.658 0.017
Inflation rate -2.465 0.034 3.686 -5.265 0.036
Population growth rate -0.582 0.146 0.845 -1.486 0.028
Dependent variable: Real Estate Growth
The multiple regression can therefore be applied as follows;
GE= f (pop growth, inflation rate, interest rate, GDP)
Therefore
GE= ? + X1GDP + X2interest rate + X3inflation rate + X4pop + µ
GE= 14.68 + 3.165GDP – 1.203interest – 2.465inflation – 0.582pop
4.2.2 Model interpretationThe model showcased above clearly shows a dependent relationship that the growth of real estate has on inflation rate, interest rate, GDP and the population growth rate whereby the growth in real estate investment stands at 14.68%. The model further show that a percentage change in interest rate would result into a 1.23% change in the growth of real estate where it is indicated as having a negative relationship with the dependent variable in question, in addition to this it is also clearly indicated from the relationship between the inflation rate and the real estate growth that a one percentage change in the former would lead to a 2.465% change in the latter and also the two have an inverse relationship. A one percentage change in population growth leads to a 0.502% change in the growth of real estate and the two also registered an inverse relationship from the model above, With regards to the independent variable GDP the model above depicts a positive relationship between it and the dependent variable whereby a one percentage change in GDP would lead to 3.165% change in the growth of real estate.
Table 4.5: Correlation Coefficients Real estate GDP Interest rate Inflation rate Pop Real estate growth Correlation 2 0.786 -0.695 -0.821 -0.345
0.052 0.071 0.054 0.32
N 16 16 16 16 16 GDP growth Correlation 0.945 2 -0.782 0.362 0.52
0.340 0.920 0.762 0.6
N 16 16 16 16 16Interest rate Correlation -0.845 -0.724 2 -0.421 0.845
0.051 0.201 0.372 0.046
N 16 16 16 16 16Inflation rate Correlation -0.741 0.321 -0.219 2 -0.47
0.462 0.958 0.362 0.58
N 16 16 16 16 16Population (POP) Correlation -0.388 0.516 0.821 -0.440 2
0.767 0.674 0.546 0.578
N 16 16 16 16 16Source: Economic survey (2004-2016)
The above table clearly shows the relationship that exists between the dependent variable and the independent variables where the negative sign depicts the inverse relationship while the positive relationship brings out the direct relationship.

Table 4.6: Variable indicatorsVariable Expectation Regression Correlation
Inflation rate _ + _
Interest rate _ + _
Population growth rate _ + _
GDP growth _ + _
Results interpreted from the table above can clearly show the relationship that exists between the variables where the GDP has a positive relationship with the real estate whereas the interest rate, inflation rate and the population growth all have a negative relationship with respect to the real estate growth.

Table 4.7: Test of abscorndancyModel R R Square Adjusted R square Std.Error1 0.721 0.865 0.942 1.462
Predictors; population growth rate, inflation rate, interest rate, GDP growth
Dependent variable; Real estate growth
The above table clearly show the dependence of real estate growth on interest rate, inflation rate, population growth and GDP growth since they have a more than 5 % chance of being greater than the t statistics value if the table would be correctly used under the analysis.

Table 4.8: Test statistical analysis (ANOVA)Model Squares df Mean square F Sig
Regression 32.24 5 6.925 26.84 0.018
Residual 1.68 12 0.368
Total 33.94 17
The above table shows a significant influence and effect that the variables that were under the analysis had an immense outcome especially on how the dependent variable would turn out to be with regards to whether they has an inverse or direct relationship as the analysis has brought out the results.
CHAPTER FIVECONCLUSION AND RECOMMENDATIONS5.0 IntroductionThis chapter starts with a discussion of the findings in relation to study objectives where the main factors influencing the growth in real estate are discussed. Factors affecting growth in real-estate included the GDP Growth, inflation growth, interest rates and Population growth. In addition the chapter summarized findings based on the data analysis. These findings were with respect to the contribution of each independent variable to the growth in real estate, summary of the statistical significance of each variable and test of explanatory ability of the independent variables. The chapter further gives policy recommendation based on how to regulate the GDP Growth, interest rates, inflation growth and population growth so that the growth in the investment can be sustained. Finally the chapter draws conclusions from the study findings and ends up by recommending areas for further research.

5.1 Summary of findingsMultiple Regression analysis had four variables namely; GDP, interest rates, inflation rates and population growth. The effect of each variable on the growth in real estate was as follows: GDP had a positive contribution to real estate investment as expected by theory. On the other hand Interest rates showed a significant negative relationship with real estate investment which was in line to the study’s expectations. Similarly, Inflation growth related negatively to real estate investment as the theory postulated. In addition, though their effect was insignificant, population growth related negatively to real estate investment. The reason for an inverse relationship between population growth and real estate was due to the fact that most of the houses build are for middle and upper income earners whereas majority of the population are low income earners. The Multiple regression provided results that were fairly in line with the expectations of the study and the literature. Most of the results from correlation analysis did support the study expectations and depicted a strong association between the dependent variable and the independent variables except for population growth results which depicted a very weak association. The adjusted coefficient of multiple determinations depicted high joint explanatory hypothesis that GDP growth contributed the most to the growth in real estate. Test for the violation of the basic econometric assumptions revealed the absence of serial auto correlation and the absence of multi collineality.

Table 5.1: Means, standard deviation Descriptive StatisticsMean Std.deviationN
Real estate and renting services 3.247 0.7492 15
GPD growth rate 3.693 2.1825 15
Interest growth rate 16.8120 3.68568 15
Annual average inflation rate 8.2653 4.65025 15
Population growth rate 3.1767 1.20109 15
Source: Economic Surveys 1997 – 2016
From Table 5.1 above it can be deduced that the annual average real estate growth was 3.247% with standard deviation of 0.7492. Similarly, GDP grew at an annual average of 3.693% with standard deviation of 2.1825. In addition the annual average lending rate was at 16.812% with a standard deviation of 3.68568. Besides, annual inflation growth was at 8.2653% with a standard deviation of 4.65025. The average annual population growth rate was at 3.1767% with standard deviation of 1.20109. The results indicated that real estate and renting business service had low annual growth. Similarly, there had been slow economic growth. However inflation, interest rate and population growth had been on a rising trend annually.

5.2 Limitation of the studyData on major variables deemed necessary for the study such as GDP, inflation rates, and interest rates and population growth as well as real estate were not readily available. In addition the period on which the study was premised is relatively short to provide a good data set for sound conclusions to be drawn from the study. However, effort was made to get monthly economic data from economic surveys and Statistical Abstract for the key variables, hence increasing the variability, validity and testability of the data.

5.3 ConclusionFrom the results it was concluded that GDP; interest rates and inflation rates were the major determinants of real estate investment at the 0.05 level as per the SPSS fitted model. Besides GDP growth contributed the most to the growth in real estate in Kenya. Population growth had a statistically insignificant negative impact on real estate investment. GDP was positively related to real estate investment whereas interest rates and inflation rates were negatively related to the growth in real estate. Factors such as Interest rates, GDP and inflation rate had statistically significant influences on real estate investment. Policy measures geared toward improving the economic growth and curbing rising inflation rates and interest rates should be undertaken as they increased the investment levels.

5.4 RecommendationsThe government through the central bank regulates the interest rates and inflation growth via the monetary policy. Monetary policy is the process by which the Central Bank influences the level of money supply credit in the economy in order to minimize excessive price fluctuations, and promote economic growth. Monetary policy guards against inflation and ensures stability of prices, interest rates and exchange rates. This protects the purchasing power of the Kenya shilling and promotes savings, investment and economic growth. Through the monetary policy, the Central Bank creates conditions that allow for increased output of goods and services in the economy, thereby improving the living standards of the people. The Central Bank through the monetary policy formulates a policy to expand or contract money supply in the economy after detailed analysis and estimation of the demand for money in the economy. The following instruments are used to conduct monetary policy in Kenya:
Reserve Requirement: commercial banks are required by law to deposit 6% of their deposits with the CBK. This is used to influence the amount of loans banks can advance the public and thus affects the supply of money. An increase in this proportion reduces the amount of money available for commercial banks to lend while a reduction has the opposite effect. The central bank decreases the proportion of reserve requirement in order to increase the money supply in the economy.

Open Market Operations (OMO): Central Bank buys and sells Government securities in the money market in order to achieve a desired level of money in circulation. When the Central Bank sells securities, it reduces the supply of money and when it buys securities it increases the supply of money in the market.

Lending by the Central Bank: The Central Bank from time to time lends to commercial banks overnight when they fall short of funds thus affecting the amount of money in circulation and the amount deposited by banks at the CBK. In order to reduce interest rates the commercial bank lowers the lending rates to commercial banks which in turn reduces the lending rates to investors.

· Moral Suasion: The Central Bank persuades commercial banks to make decisions or follow certain paths to achieve a desired result like changes in the level of credit to specific sectors of the economy. Some other measures that help to curb rising interest rates and inflation include the following: Establishment of a well-developed financial sector, including a more integrated microcredit sector. This can help expand access to an array of financial services (credit and insurance; saving facilities and payment instruments). This helps to finance small private firms at rates that do not cripple their operations.

Alternative Technologies: Using alternative technologies can be challenging in the Kenyan market, but if done correctly it has the potential to be an essential piece of bringing down the cost. The most important aspect to be aware of is ensuring that the look and feel of the home is similar, if not the same, as traditional techniques. When someone purchases a home, whether they are rich or poor, they want to put their savings into old-fashioned brick and mortar rather than a shiny new technology that is untested and unfamiliar. The ministry of housing disseminates information on low cost housing technology through the establishment of appropriate building technology centers in each constituency.

Avoiding speculation
This entails a careful development of strategies to avoid speculation from the outset. Some potential strategies include:
i. Developing strict criteria for buyers to qualify
ii. Ensuring owner-occupation within a short time period
iii. Limiting the number of homes that can be purchased by one individual
iv. Withholding title deed for a period of time, such as 5 years, so owners are unable tore-sell.

Develop contracts with suppliers
Given the volatile economic environment, materials prices can skyrocket and turn a healthy project completely unviable. In order to avoid this, develop fixed rate contracts with materials suppliers whenever possible. Some may even have Corporate Social Responsibility programs, so do not be afraid to market the social impact side of the project in order to negotiate better terms.

5.5 Areas for further researchSome of the areas for further research may include the following:
l. The impact of the cost of finance on the construction industry.

2. The effect of the population growth on real estate development.

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Wiesbaden.APPENDICESAppendix 12014 2014 2015 2015 2016 2016
JANUARY 290,805 264,849 364,432 364,711 350,615 272,261
FEBRUARY 266,889 178,789 335,247 291,531 378,453 330,328
MARCH 300,610 223,450 355,858 289,361 397,009 328,259
APRIL 284,987 258,016 363,035 319,710 360,540 308,617
MAY 294,158 250,016 376,246 330,084 381,026 309,501
JUNE 312,176 274,073 365,494 295,465 396,951 329,366
JULY 334,444 273,581 393,149 326,419 398,458 337,295
AUGUST 323,478 284,184 405,546 342,284 399,873 339,916
SEPTEMBER 319,464 253,692 407,838 302,720 382,191 322,912
OCTOBER 351,963 309,066 361,941 341,277 421,579 365,769
NOVEMBER 323,447 281,564 364,789 327,859 415,866 382,400
DECEMBER 307,385 252,726 384,853 339,516 357,212 310,639
TOTAL 3,709,807 3,104,827 4,478,428 3,870,930 4,639,723 3,937,263
Source: monthly Economic Indicators 2016
Appendix ii
VALUE OF BUILDINGS APPROVED BY NAIROBI CITY COUNCIL
ACTUAL REAL
RESIDENTIAL NON RESIDENTIAL AGGREGATE RESIDENTIAL NON RESIDENTIAL AGGREGATE
2015 NOV 13,804.50 4,397.1 18,201.5 235.8 71.4 303.6
DEC 12,497 5,186.7 17,684.5 213.5 84.3 295.0
2016 JAN 3,787.50 4783.3 8570.8 60.4 75.6 134.3
FEB 5,078.9 4,606.1 9685.0 81.0 72.8 151.7
MAR 3,756.9 12,616 16,372.9 59.9 199.5 256.5
APRI 5319.8 12,700.9 18,020.70 84.8 200.9 282.4
MAY 5,589.5 4204.2 9793.7 89.2 200.9 282.4
JUNE 5712.7 7,349.9 13,062.6 91.1 116.2 204.7
JULY 5921.3 11,636.1 17,564.4 99.5 184 275.2
AUG 6685 14,775 21,460 106.6 233.7 336.2
SEPT 8,497.5 12,809.7 21,307.3 135.5 202.6 333.9
OCT 6,371.9 14,506.9 20,886.0 101.7 229.4 327.3
NOV 11,546 3,008.0 14,554.0 84.1 47.6 228.0
DEC 6,963.7 12,273.5 18,298.4 110.1 194.1 286.7
Source: Nairobi city council (2015-2016)
Appendix
TIME SERIES DATA ON 91 DAYS TREASURY BILL INTEREST RATES
Year JAN FEB MARCH APRIL MAY JUNE JULY AUG SEPT OCT NOV DEC
2001 14.76 15.30 14.97 12.90 10.52 12.07 12.87 12.84 12.39 11.63 11.50 11.01
2002 10.85 10.61 10.14 10.01 9.04 7.34 8.63 8.34 7.60 8.07 8.30 8.38
2003 8.38 7.77 6.24 6.25 5.84 3.60 1.54 1.18 0.83 1.00 1.28 1.46
2004 1.58 1.57 1.59 2.11 2.87 2.01 1.71 2.27 2.75 3.95 5.06 8.04
2005 8.26 8.59 8.63 8.68 8.66 8.50 8.59 8.66 8.58 8.19 7.84 8.07
2006 8.23 8.02 7.60 7.02 7.01 6.60 5.89 5.96 6.45 6.83 6.41 5.73
2007 6.00 6.22 6.32 6.65 6.77 6.53 6.52 7.30 7.35 7.55 7.52 6.87
2008 6.95 7.28 6.90 7.35 7.76 7.73 8.03 8.02 7.69 7.75 8.39 8.59
2009 8.46 7.55 7.31 7.34 7.45 7.33 7.24 7.25 7.29 7.26 7.22 6.82
2010 6.56 6.21 5.98 5.17 4.21 2.98 1.60 1.83 2.04 2.12 2.21 2.28
2011 2.46 2.59 2.77 3.26 5.35 8.95 8.99 9.23 11.93 14.80 16.14 18.30
2012 20.56 19.70 17.80 16.02 11.18 10.09 11.95 10.93 7.77 8.98 9.80 8.25
2013 18.24 8.67 14.56 14.56 10.98 7.56 5.68 6.54 2.48 10.42 4.46 2.46
2014 4.67 5.96 3.96 12.98 8.56 14.98 4.54 8.98 6.54 5.62 7.68 1.86
2015 7.66 12.48 10.98 8.56 2.56 10.54 3.46 2.54 7.85 10.78 9.68 7.42
2016 9.75 16.45 9.76 9.21 2.89 5.62 8.96 4.68 12.88 9.82 12.96 8.45
Source: Central Bank of Kenya (2016)