20th century music :: essays research papers

By the turn of the century and for the next few decades, artists of all nationalities were searching for exciting and different modes of expression. Composers such as Arnold Schoenberg explored unusual and unorthodox harmonies and tonal schemes. French composer Claude Debussy was fascinated by Eastern music and the whole-tone scale, and created a style of music named after the movement in French painting called Impressionism. Hungarian composer Bà ©la Bartà ³k continued in the traditions of the still strong Nationalist movement and fused the music of Hungarian peasants with twentieth century forms. Avant-garde composers such as Edgard Varà ¨se explored the manipulation of rhythms rather than the usual melodic/harmonic schemes. The tried and true genre of the symphony, albeit somewhat modified by this time, attracted such masters as Gustav Mahler and Dmitri Shostakovich, while Igor Stravinsky gave full rein to his manipulation of kaleidoscopic rhythms and instrumental colors throughout his extremely long and varied career. While many composers throughout the twentieth-century experimented in new ways With traditional instruments such as the "prepared piano" used by American composer John Cage, many of the twentieth-century's greatest composers, such as Italian opera composer Giacomo Puccini and the Russian pianist/composer Sergei Rachmaninoff, remained true to the traditional forms of music history. In addition to new and eclectic styles of musical trends, the twentieth century boasts numerous composers whose harmonic and melodic styles an average listener can still easily appreciate and enjoy. The advance of technology has also had an enormous impact on the evolution of music in this century, with some composers using, for instance, the cassette player as a compositional tool or electronically generated sounds alongside classical instruments, the use of computers to compose music, and so on.

Safety On Board Ships Engineering Essay

Safety is of extreme importance onboard ships. There have been many ship related accidents and incidents that have claimed many lives. One such common accident would be fire eruptions onboard ships. Due to the high figure of such fatal happenings, MARPOL and SOLAS have been erected, and they contain regulations and ordinances that all mariners must stay by. In order to forestall incidents, safety equipments are normally installed in the ships. One of such safety equipments would be sensors. Detectors are devices that can observe fire or some other risky conditions. Onboard ships, there are several different types of sensors. They are as follows: Fire Detector Flammable Gas Detector Refrigerant Gas Leak Detector Water Level sensor 1. Fire Detector Fire sensors are used to observe fires onboard ships. Fire is a common jeopardy that happens at sea. Statistically, fire eruptions have resulted in more entire losingss of ships than any other signifier of casualty. Most of the fires are caused by carelessness and sloppiness. Fortunately, with fire sensors, the extent of harm caused by fires can be mostly minimised. A good fire sensor is one that is dependable and requires minimal attending. More significantly, the fire sensor must non be set off by normal happenings in the protected infinite, hence its sensitiveness must be adjusted accurately and in conformity to its surrounding. Under fire sensors, there are 3 sub types: Smoke sensor Flame sensor Heat sensor a ) Smoke sensor – The two types of fume sensors: Ionisation sensor and photoelectric sensor. The ionization sensor reacts to both the seeable and unseeable merchandises of burning, but the photoelectric type merely responds to seeable merchandises of burning. Ionisation smoke sensors make usage of ionization chamber and a beginning of ionization radiation to observe fume. There are two types of ionization fume sensors. One type uses a bipolar ionised trying chamber, and the other uses a unipolar ionized trying chamber. The beginning of ionization radiation comes from a little sum of americium-241, which is a good beginning of alpha atoms. The bipolar ionised trying chamber type fume sensor has an ionization chamber that contains two electrodes of a possible difference as a electromotive force is applied across them. In between the two electrode is air-filled infinite. The alpha particles that are being produced by the americium-241 ionises the air in between the two electrodes. To ionize agencies to strike hard off an negatron from an atom. This creates a free negatron and a positively-charged ion. The free election will so be attracted to the positively-charge electrode and the positive ion will be attracted to the negatively-charged electrode, due to the possible difference between the two electrodes. This, hence, produces a changeless flow of current between the electrodes. When a minute measure of fume enters the air-filled infinite in between the electrodes, the ionized air atoms get neutralised by the fume atoms. This will ensue in a autumn of current between the electrodes. The fume sensor detects this bead in current and sets off the fire dismay. The 2nd type of ionization fume sensor has a unipolar ionized trying chamber alternatively of a bipolar 1. The lone difference is that for the bipolar one, the whole chamber is exposed to the radiation, whereas for the unipolar 1, merely the immediate country adjacent to the positive electrode is exposed to the alpha beginning. As a consequence, the unipolar type has merely one prevailing type of ions, which are anions, in the electrical current flow between the electrodes. Presently, the unipolar type fume sensors are the commercially most common 1s. One of the few drawbacks of ionization type fume sensors is that there may be frequent false dismaies. The ground being that any micron-size atom, such as kitchen lubricating oil atoms, come ining the ionization chamber can really put off the dismay. However, this type of fume sensors are still the most normally used, due to their dependability, low cost and comparatively maintenance-free operation. Smoke Detector There are two chief types of photoelectric fume sensors, viz. the projected beam type and the reflected beam type. Photoelectric smoke sensors work on footing of the presence or absence of visible radiation. The projected beam type consists of a photoelectric detector with light falling on it from a beginning located at holds or other protected infinite on the ship. When there is the presence of fume, the light strength of the beam that is received in the photoelectric cell lessenings due to it being obscured by the fume atoms. This decreased degree of light strength causes the electrical circuit to the photoelectric cell to be imbalanced, and therefore triping the dismay. The reflected light beam type fume sensor consists of a light beginning, a light backstop positioned face-to-face to the light beginning and besides a photoelectric cell fixed normal to the light beginning. When fume particles enter into the light beam part, some visible radiation is being reflected onto the photoelectric cell. This creates a closed circuit, and therefore puting off the dismay. Photoelectric sensors are normally used to protect storage countries and high value compartments, and besides to supply fume sensing for air canals and plenum countries. However, the downside of this type of photoelectric fume sensor is that the fume has to be thick before it can be detected. This is due to its comparatively low sensitiveness. The plus side of this type of fume sensors is that there will be fewer false dismaies. Smoke sensors are chiefly used in machinery infinites, lading holds and adjustment countries. All ships built since September 1985 are required to be provided with smoke sensors in corridors and over staircases within adjustment infinites. Both the ionization and photoelectric fume sensors are effectual as they provide sufficient clip for people to get away in the instance of a fire eruption. Each type of fume sensor, though different in working rules, has its ain advantages. For illustration, ionization fume sensors have a response quicker for flaring fires. As for photoelectric sensors, they respond more rapidly to smoldering fires. To guarantee the high degree of protection, it is advised to utilize both types of sensors. There are combination dismaies, that contains both type of engineerings in one device, and it besides can be employed to accomplish higher protection. B ) Flame sensor – Fires are normally caused by gas and liquid fires. Flame sensor uses optical detectors to observe fires. Fires give off radiation dwelling chiefly of ultra-violet radiation, seeable visible radiation and infrared radiation. There are about 6 types of fire sensors, which consist of UV ( UV ) , infrared ( IR ) , UV/IR, IR/IR, IR/IR/IR and seeable detectors. Ultraviolet sensors are able to observe fires and detonations in approximately 4 msecs. When a little fire is ignited, an ultraviolet sensor can instantly separate the type of fire it is. Even though they are really accurate, ultraviolet sensors can be fooled by radiation, discharge welding, sunshine and lightning. An infrared fire sensor plants by utilizing an infrared set. When hot gases are released near an infrared sensor, The little thermic imaging camera within the sensor will so pick up on the presence of these gases. However, false dismaies can be set off when other wanted beginnings of hot gas are present near an infrared fire. UV/IR sensor plants by utilizing a combination of UV and IR engineering to observe a fire. Such a sensor gathers information from the UV and infrared position. With these two engineerings working together, false dismaies can be minimised. The similar rule applies to IR/IR fire sensor. It detects flames within two infrared frequences. Hence, IR/IR sensors are besides able to extinguish most false dismaies. The IR/IR/IR sensors are the most accurate. They use three different infrared frequences used to observe a fire. IR/IR/IR sensors work by comparing three wavelength sets, therefore, it is extremely improbable for this type of a fire sensor to give off a false dismay. Often, in order to observe seeable fires, seeable detectors are besides installed in with the fire sensor. Hence, when a fire occurs, flame sensors are able to observe the radiations, and will so put off the dismay. Fire sensors are normally used near to fuel managing equipment in the machinery infinites and besides at boiler foreparts. Flame sensor type parts Infrared Flame Detector degree Celsius ) Heat sensor – It is a device that responds when the thermic energy of a fire increases the temperature of a heat sensitive component. Heat sensors have two chief categorizations: Fixed temperature and Rate-of-rise. Fixed temperature heat sensors operates when the heat sensitive component in it reaches a certain fixed temperature. Thermal slowdown delays the accretion of heat at the heat sensitive component so that the device will merely make the operating temperature sometime after the encompassing temperature exceeds that temperature. When the fixed operating temperature of the heat sensitive component is reached, the dismay connected to the heat sensor will be set off. Rate-of-rise heat sensors activates when there is a rapid rise in temperature of the heat sensitive component, normally about 6.7a? °C to 8.3a? °C addition per minute. This type of heat sensors work irrespective of the starting temperature. This would intend that the rate-of-rise heat sensor may put off the dismay before the fixed operating temperature is reached. Presently, most heat sensors use the bimetallistic strip mechanism. The bimetallistic strip is made up of two strips of metal stuck together, and each have different rate of enlargement. When there is a rise in temperature, one strip will spread out more than the other. This causes the bimetallistic strip to curve. The coil will ensue in the strip touching a contact that will shut the circuit, and therefore bring forth a current flow, which will so put off the dismay. The newest type of heat sensor is called the rate-compensated sensor. It is sensitive to both the rate of rise of temperature, and besides a fixed temperature degree, both of which are illustrated above. Heat sensors are rarely used because of the trouble in proper arrangement comparative tenancy environment and jeopardy countries. Heat sensors are chiefly used in topographic points such as the galleys and the wash where other types of fire sensors will give off false dismaies. Heat Detector Fire sensors are placed all over any marine vas. However, different types of fire sensors are suited at different locations. In the work store country, welding plants invariably produces fume and bare fires. Hence, a heat sensor would be most suited or none should be placed in this country as it is a certified hot work country. In the engine control room, fume sensors are used. At parts near boilers and incinerators, a bare fire can be produced due to unnatural conditions. Hence, the most suited types of fire sensors would be the ionisation type fume sensor and infrared fire sensor. Smoke sensors are by and large used throughout the engine room. The fire sensors are used near fuel managing units like refiners, purifiers, conditioners and hot filters. 2. Flammable Gas Detector Flammable gases are gases that at ambient temperature and force per unit area, forms a flammable mixture with air at a concentration of 13 per centum by volume or less. Some illustrations of flammable gases that are normally found in ships are hydrocarbon gases, H sulfide and O. Flammable gas sensors will pull samples of air sporadically, and analyze them for chiefly hydrocarbon gas and besides other flammable gases. If the gas concentration is above the pre-set dismay threshold, an dismay will sound off instantly. Flammable gas sensors, though non compulsory, are normally installed in enclosed infinites which can keep high volumes of flammable gases. The danger of lading leaks into null infinites and ballast armored combat vehicles, and the hazard of detonations associated with a physique up of hydrocarbon gas is something to be taken earnestly. Flammable gas sensors are sometimes besides installed at adjustment air conditioning recess. This is to forestall fire eruptions to go on in countries where there are changeless human activity. Harmonizing to SOLAS Chapter II, 2 Regulation 5.10.1, â€Å" Protection of lading pump-rooms † . It is a mandatory ordinance that is applicable all types of oilers that carry ladings with a flash point of below 60A °C in relation to cargo pump room safety. In order to observe leaks, the ordinances states that hydrocarbon gas sensing are to be installed within the pump room, with dismay being pre-set at no more than 10 % Lower Explosive Limit ( LEL ) . LEL of a vapor or a gas is the restricting concentration ( in air ) that is required for the gas to light and detonate. 3. Refrigerant Gas Leak Detector Refrigerant gases are chemical merchandises used in deep-freezes, iceboxs, air conditioning units. These gases have low vaporization points, hence they will distill under force per unit area to chill the air. The perennial procedure of vaporizing and distilling the gases pulls heat out of the air, therefore cut downing the temperature of the in the unit. There are many different types of refrigerant gas, and the more common 1s include CFC ( CFC ) , HCFC ( HCFC ) , HFC ( HFC ) , perfluorocarbon ( PFC ) , and blends made from ammonium hydroxide and C dioxide. However, instances of escape of refrigerating gases is a common sight. Some refrigerating gases are damaging to our environment. For illustration, when CFC is released into the ambiance, a chemical alteration will take topographic point due to its exposure to the UV visible radiation. This reaction will ensue in the production of green house gases, and besides depletes the ozone bed. Bing able to observe refrigerant gas escape can assist cut down on unneeded disbursals and besides assist protect the environment. Harmonizing to MARPOL Annex VI Regulation 12 – ozone depletion substances, refrigerant gas sensors are to be installed to supervise and observe any escapes of refrigerating gases. Refrigerant gases are continually monitored by fixed gas detectors. When the sensor detects that the refrigerant gas concentration exceeds a certain prefixed bound ( e.g. 25 ppm for ammonium hydroxide, 300 ppm for halogenated fluorocarbons ) , the dismay will be set off, alarming whoever manning the system. Refrigerant gas sensors are normally located in topographic points where the refrigerant are likely to leak, such as the centralized lading infrigidation systems, centralised air conditioning systems and centralised domestic infrigidation systems. 4. Water Level Detector Water escape and immersion may go on onboard ships. When lading holds or bulkhead are filled with extra H2O, it will damage the lading onboard and besides badly affect the perkiness and stableness of the ship. Worst instance scenario would be the implosion therapy of the ship, taking to it droping. Hence, H2O sensors are of high importance, and are used to observe if the H2O degree, in any compartment, exceeds over a preset tallness. Harmonizing to SOLAS XII Regulation 12 and SOLAS Regulation II-1/23-3, majority bearers and general lading vass are required to be installed with H2O degree sensors. Water degree sensors means a system consisting detectors and indicant devices that detect and warn of H2O immersion in lading holds and other infinites as required. The method of observing the H2O degree may be by direct or indirect agencies. Direct agencies of sensing determine the presence of H2O by physical contact of the H2O with the sensing device. Indirect means include devices without physical contact with H2O. Water sensors are positioned at a preset tallness at the aft terminal of each person lading clasp or compartment. The height place specifications are different between majority bearers and lading vass. When the H2O degree in any peculiar compartment reaches the dismay degree, the sensor will observe it, and the dismay will be set off. The image below is an illustration of the place of the H2O sensor detectors. hypertext transfer protocol: //www.km.kongsberg.com/KS/WEB/NOKBG0397.nsf/AllWeb/51C66AA6A4CD0F2BC1256EA7004D1E89/ $ file/c200wid_ae.pdf? OpenElement Decision For the safety of lives out at sea, and the protection of our environment, different types of sensors have been invented and installed onboard ships. The chief sensors that can be found in any ships are those explained above, which are the fire sensor, flammable gas sensor, refrigerant gas sensor and the H2O degree sensor. There are many other different types of sensors that uses different types of mechanisms, but still function same intent as those stated supra. Equally long as the sensors are able to function their map and are besides in conformity with MARPOL and SOLAS ordinances, they will be permitted excessively.

Real Estate Prices In Inner And Outer London - Free Essay Example

Sample details Pages: 14 Words: 4216 Downloads: 2 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Abstract Understanding and predicting upcoming real estate house prices is a vital activity that forms the center for all strategic, tactical and operational planning decisions in management of real estate businesses. The political, environmental, sociological, technological, legal and economic factors in the market affect the consequent house prices; but the affect is greater in Inner London as compared to Outer London. In times of economic boom and political stability; Inner London is an attractive residence for professional and skilled individuals and families. Don’t waste time! Our writers will create an original "Real Estate Prices In Inner And Outer London" essay for you Create order Living in Inner London offers a close proximity to the large workplace; and a dwelling for like-minded professionals that work in similar markets, i.e. financial services. This study examines how best to model and forecast house prices through times series analysis to identify the accuracy of the forecasts and the smoothness of prices. Chapter 1: Introduction The macroeconomic or financial economics perspective emphasize housing as investment in an augmenting life-cycle model of consumerism, with minimal consideration to spatial issues; implying that houses and related facilities are crude human necessities that are utilized cost-effectively. The urban economics perspective, places spatial issues at the centre, denoting human desire affect selection of houses and associated amenities. The current explanations given by mortgage banks for large house price increases is that the class of consumers who are not constrained in their access to credit are speculating and trading in this profitable market. Illustration of housing as an asset, consumers only pay a high price if it implies that at the time of sale in the future a consumer will be prepared to pay a high price; this is the reason that price of housing should reflect the future. A fall in nominal interest rates leads to consumers capitalizing in the house market thereby causing large debts and high house prices. 1.1 Hidden Layers in House Sales Market The presence of hidden layers in the housing framework of prices and consumption shields analysts against the casual explanation that house prices adjust in alignment to fundamental reasoning; the actual archetype is then based on bubble reasoning. The economic, socio-political situation of the free market guides investors and sequentially the real prices in the real estate market. An analysis of market efficiency leads to investors or consumer to speculate in housing; the most important determinants of long-run national and sub-national house prices are in consequence to; real interest rates, housing stock, demographic changes, credit availability, and tax structure. 1.2 Benefit of forecasting future house sales Forecasting future house sales is one of the most important activities that form the basis for all strategic and planning decisions in effective operations of real estate businesses and the real estate market economies. The best model to forecast house sales is through a time series that contain both trend and seasonal variations. Sequentially, accurate forecasts of consumer or investor house sales can help improve real estate market operation especially for larger agencies that have a significant market share. For profitable house sale operations, accurate demand forecasting is crucial in organizing and planning for consumers or investors; construction/renovation aspects, marketing and administration, as well as after-sales services. A poor forecast would result in either too much or too little customers, directly affecting the profitability of the brand and the competitive position of the organization. 1.3 Demographic Factors of UK House Market Trends in demographic factors maintain demand, thus aiding in the explanation into the reasons housing has a good historical rate of return. The recent shift upwards of price trend into a new permanently higher level implies a shifting upwards in the trend of the original demographic factors. Whilst the trends keep adapting to economic scenarios, the position of the trends has not shifted radically. Realistically the demographic factors complimentary to house prices in the 1980s largely relaxed in the early 1990s. Recent studies by the IMF have suggested that the house-holds variable affects the long-term prices, and is not a significant issue in the short-term determination of prices. 1.5 The Nominal Interest Rate UK mortgage banks are placing significant spotlight on nominal interest rate, however, no valid economy statistics report has determined that the nominal interest rate plays any role in the long-run determination of house prices. Essentially it is the real costs of housing that matter, though in the short-run real house prices do respond to changes in nominal interest rate. Consumers purchase pricey houses by engaging in debts; the same initial monthly repayment is due; thus when nominal interest rates are 1% (all real rates remain the same) house prices augment, the percentage of lifetime income spent on housing increases and amount spent on other goods and services decreases, implying that just one or two per cent more interest can make a major variance to all consumption potentials. 1.6 Real Interest Rates As market study and theory implies that at a low real interest rate, more customers borrow more money and house prices rise. Risk-averse consumers (credit unconstrained) base decision of amount of money to borrow on: 1) Real 2) After Tax 3) Expected and 4) Risk-Adjusted Rate of Interest. 1.7 Credit Constraints of House Buyers The presence of credit constraints in the analysis designates inconsistencies that suggest (even without examining for market efficiency) that the clarification is attained from supplementary factors or hidden layers. Simplistic explanations of house prices do emphasize that it is the intensification of à ¢Ã¢â€š ¬Ã‹Å"lifetime utilityà ¢Ã¢â€š ¬Ã¢â€ž ¢ that drive analysis and from this correct house prices are predictable. 1.8 GDP Growth and Expectations At times that consumers are more optimistic about their economic prospects, increase in consumption of other goods and services in alignment with an increase in house prices occurs. House prices and the growth of debt-driven consumption are a result of unrealistic inference to recent unemployment and income patterns. The origins of growth matter; recent UK economic performance is based on consumption that is relatively based on high house prices; each time the housing market collapses, unemployment rises. Market study reveals that a large percentage of decrease in unemployment is from extension in government employment and from the housing market. Also, Long-term increases in the productivity of UK markets pulses into house prices. Chapter 2: Literature Review Precise forecasts of consumer real estate prices aide in improving real estate business operation, especially for agencies that have a significant market share. For cost-effective real estate operations, perfect price forecasting is decisive in organizing and planning purchasing, production, transportation, and labor force, as well as after-sales services. A pitiable forecast results in either too much or too little preparation, directly upsetting the prosperity of the agency chain and the competitive situation of the business. 2.4 Real Estate Investments Real estate investments are categorized into: 1) private equity, 2) public equity, 3) private debt and 4) public debt. A choice of which one to invest in depends on the type of exposure required for a portfolio; to invest in income-producing properties or non-income-producing properties. Any leased property is income producing, and vacant properties are non-income producing. It is possible to earn a capital return on a non-income producing property, just as on an investment in a home. The foremost types of investment properties are offices, retails, industrials and multi-family residential properties. Real estate can produce income (like a bond) and appreciate (like equity). Real estate is tangible, thus requires constant management. There is an increased ability to influence the performance of a single investment as compared to other asset classes. Some of the benefits of adding real estate to a portfolio include: diversification, yield enhancement, risk reduction and inflation-hedging capabilities. However, real estate also has high transaction costs, can be difficult to acquire and it is challenging to measure its relative performance. Buying real estate requires considerable positive intuition to ensure that the investment is beneficial. To determine the value of a property (other than actually selling it) is to have it appraised by an accredited appraiser. 2.5 Private or Public Markets In the planning of real estate investments, a first tasks is to decide what kind of exposure to the real estate market is appropriate for the situation. Different exposures produce varying levels of risk and return. The choice also influences the means by which real estate is acquired. The first type of market is the private market. In the private market; involves purchasing a direct interest in one or more real estate properties; own and operate the piece of real estate through a property manager or by-self, and receive the rent payments and value changes from that investment. Participation in this market is also possible by purchasing properties with any number of partners, known as a pool or syndicate. Alternatively, participating in the public market is purchasing a share or unit in a publicly traded real estate company. Purchase of a real estate security institutes investing in a company that owns real estate and manages it on behalf of the shareholders/unit-holders of the company. As a result, overall exposure to the real estate market is more indirect. A real estate security usually pays a dividend or distribution in order to send the rent payments that it receives from tenants to its shareholders/unit-holders. Any price appreciation or depreciation in the assets owned by the company is reflected in its share or unit price. 2.6 Equity and Debt Investments In addition to choosing the market, another decision is whether to invest in debt or equity. An investment in debt; lending funds to an owner or purchaser of real estate; receive periodic interest payments from the owner and a security charge against the property in the form of a mortgage. At the end of the mortgage term, the bank (conventionally) obtains back the balance of the mortgage principal. This type of real estate investing is quite like that of bonds. An equity investment represents a residual interest in the property. An equity investor is effectively the owner of the property; situated to gain reasonably if the property value increases or if rent for the building increases. However, in the event of tribulation (i.e. all tenants vacate and it is not possible to make the mortgage payment) then the mortgagee (has a priority interest in the property), may foreclose and the investor must forfeit the equity position to satisfy security. In that sense, the risks of an equity position in real estate is much like that of owning stock. The choice of whether to invest in equity or debt depends upon the investorà ¢Ã¢â€š ¬Ã¢â€ž ¢s risk tolerance and return expectations. 2.7 Types of Real Estate Real estate investments have one or more tangible real estate properties underlying each investment; in an investment it is important to consider the characteristics of the principal real estate because the performance of those properties will impact the performance of the investment. One of the most important criteria (aside from location) is the type of property. The primary properties are; residential homes, shopping malls, warehouses, office towers or a combination of any of these. Each type of real estate has a different set of drivers influencing its performance. There are four types of income-producing real estate: 1) offices, 2) retail, 3) industrial and 4) leased residential. There are many other less common types as well; hotels, mini-storage, parking lots and seniors care housing. Non-income-producing investments, such as houses, vacation properties or vacant commercial buildings, are as beneficial as income-producing investments. If investing in equity in a non-income producing property, there is no income from rent, so all of the return is through capital appreciation. If investing in debt secured by non-income-producing real estate; the borrowers personal income must be sufficient to cover the mortgage payments, because there is no tenant income to secure the payments. Office Property Offices are the desired investment for frequent real estate owners, on average, the largest and highest profile property type due to typical location in town centers and expansive business office parks. The demand for office space is attached to companies requirement for office workers, and the standard space per office worker. The typical office worker is involved in things like finance, accounting, insurance, real estate, services, management and administration. As these white-collar jobs grow, there is greater demand for office spaces. Returns from office properties can be highly variable because the market tends to be sensitive to economic performance. One obstacle is that office buildings have high operating costs, so if a tenant is lost, it can have a substantial impact on the returns for the property. Nonetheless, in times of prosperity, offices tend to perform exceptionally sound, because demand for space causes rental rates to increase and an extended time period is required to build an office tower to relieve the pressure on the market and rents. Retail Property There is a ample variety of retail properties, ranging from large enclosed shopping malls to single tenant buildings in pedestrian zones. At the present time, the Supremacy Center format is in favor, with retailers occupying larger premises than in the enclosed mall format, and having greater visibility and access from adjacent roadways. Recurrent retail properties have an anchor, which is a large, well-known retailer that acts as a draw to the center. If a retail property has a food store as an anchor, it is said to be food-anchored or grocery-anchored; such anchors would typically enhance the fundamentals of a property and make it more desirable for investment. Often, a retail center has one or more ancillary multi-bay buildings containing smaller tenants. One of these small units is termed a commercial retail unit (CRU). The demand for retail space has many drivers. Among them are: location, visibility, population density, population growth and relative income levels. From an economic perspective, retails tend to perform best in growing economies and when retail sales growth is high. Returns from Retails tend to be more stable than Offices, in part because retail leases are generally longer and retailers are less inclined to relocate as compared to office tenants. Industrial Property Industrials are often considered the simpler of the average real estate investor; require smaller average investments, are less management intensive and have lower operating costs. There are varying types of industrials depending on the use of the building (i.e. warehousing, manufacturing, research and development, or distribution). Important factors to consider in an industrial property are functionality (i.e. ceiling height), location relative to major transport routes (including rail or sea), building configuration, loading and the degree of specialization in the space (such as whether it has cranes or freezers). For some uses, the presence of outdoor or covered yard space is important. Multi-Family Residential Property Multi-Family residential property delivers stable returns, regardless of the economic cycle, people always need a place to live. The result is that in normal markets, residential occupancy tends to stay reasonably high. Another factor contributing to the stability of residential property is that the loss of a single tenant has a minimal impact on the substructure. Commercial property types portray tenant leases either net or partially net, meaning that most operating expenses can be passed along to tenants. However, residential properties normally do not have this feature, meaning that the risk of increases in building operating costs is borne by the property owner for the duration of the lease. A positive aspect of residential properties is that; government-insured financing may be available. At the cost of a small premium, insured financing lowers the interest rate on mortgages, enhancing potential returns from the investment. 2.8 Characteristics of Real Estate Investments One of the beneficial features of real estate is that it produces relatively consistent total returns that are a hybrid of income and capital growth. In that sense, real estate has a coupon-paying bond-like component in that it pays a regular, steady income stream, and it has a stock-like component in that its value has a propensity to fluctuate. And, like all securities that the investor has a long position in, prefers the value to go increase. The income return from real estate is directly linked to the rent payments received from tenants, minus the costs of operating the property and outgoing mortgage/financing payments. If too many tenants are lost, the owner does not have sufficient rents paid by the other tenants to cover the building operating costs. The ability to keep the building full depends on the strength of the leasing market; the supply and demand for space similar to the ownerà ¢Ã¢â€š ¬Ã¢â€ž ¢s space to lease. In weaker markets with oversupply of vacancies or poor demand, the investor has to charge less rent to maintain the building full than in a strong leasing market. Evidently, if the rents are lower, income returns are lower. Capital appreciation of a property is determined by having the property appraised. An appraiser uses actual sale transactions that have occurred and other pieces of market data to estimate what the property would be worth if it were to be sold. If the appraiser declares that the property would sell for more than purchased for, then the owner has achieved a positive capital return. Because the appraiser uses past transactions in judging values, capital returns are directly linked to the performance of the investment sales market. The investment sales market is affected largely by the supply and demand of investment product. The majority of the volatility in real estate returns comes from the capital appreciation component of returns. Income returns tend to be fairly stable, and capital returns fluctuate more. The volatility of total returns distinguishes someplace in between. 2.9 Other Characteristics of Real Estate Investments Other characteristics that make real estate unique as compared to other investment alternatives are as follows: No fixed maturity Unlike a bond which has a fixed maturity date, an equity real estate investment does not normally mature. In Europe, it is not uncommon for investors to hold property for over 100 years. This attribute of real estate allows an owner to buy a property, execute a business plan, then dispose of the property whenever appropriate. An exception to this characteristic is an investment in fixed-term debt; by definition a mortgage would have a fixed maturity. Tangible Real estate is real; it is possible to visit the investment, speak with tenants, and obtain speculation. As a result, the investor holds a certain degree of physical control over the investment, dissimilar to a stock or bond. Requires Management Real estate is tangible and requires to be managed; tenant complaints are addressed, landscaping is handled, and renovation is required after passing of several or more years. Inefficient Markets An inefficient market indentifies that information asymmetry exists among participants in the market, allowing greater profits to be made by those with special information, expertise or resources. In contrast, public stock markets are much more efficient; information is efficiently circulated amongst market participants, and those with material non-public information are not permitted to trade upon the information. In the real estate markets, information is vital, and facilitates an investor to identify profit opportunities that otherwise would not be visible. High Transaction Costs Private market real estate has high purchase costs and sale costs. On purchases, there are real estate agent-related commissions, lawyers fees, engineers fees and many other costs that can raise the effective purchase price sound beyond the price the seller will essentially collect. On sales, a substantial brokerage fee is usually required for the property to be properly exposed to the market. Because of the high costs of à ¢Ã¢â€š ¬Ã…“tradingà ¢Ã¢â€š ¬? real estate, longer holding periods are common and speculative trading is rarer than for stocks. Lower Liquidity With the exception of real estate securities, no public exchange exists for the trading of real estate. This makes real estate more difficult to sell because deals must be privately brokered. There can be a substantial lag between the time the owner decides to sell a property and when it actually is sold. Underlying Tenant Quality When assessing an income-producing property, an important consideration is the quality of the underlying tenancy. This is important because when investing in the property, there are two acquisitions: the physical real estate and the income flow from the tenants. If the tenants are likely to default on the monthly obligation, the risk of the investment is greater. Variability among Regions Location is one of the important aspects of real estate investments; a piece of real estate can perform very differently amongst, regions, cities, towns. These regional differences need to be considered when making an investment, because the investors selection of which market to invest in has as large an impact on resultant returns. Chapter 3: Methodology Chapter 4: Results 4.1 Time Series Analysis on Outer London House Prices Year Price in GBP Moving Average Centered Average Seasonal Variation Seasonal Variation Multiplicative Model Q1 1996  £59,562.61 Q2 1996  £57,936.38 60,272.93 Q3 1996  £61,673.3 60,781.26 60,527.1 +1146.21 101.8937 Q4 1996  £61,919.44 62,772.85 61,777.05 +142.386 100.2305 Q1 1997  £61,595.91 64,795.35 63,784.1 -2188.191 96.56938 Q2 1997  £65,902.75 66,635.37 65,715.36 +187.39 100.2852 Q3 1997  £69,763.31 69,295.36 67,965.36 +1797.95 102.6454 Q4 1997  £69,279.5 72,263.94 70,779.65 -1500.149 97.88054 Q1 1998  £72,235.88 74,293.44 73,278.69 -1042.808 98.57693 Q2 1998  £77,777.06 76,726.72 75,510.08 +2266.98 103.0022 Q3 1998  £77,881.31 78,337.86 77,532.29 +349.02 100.4502 Q4 1998  £79,012.63 80,102.67 79,220.27 -207.6362 99.7379 Q1 1999  £78,680.44 83,818.49 81,960.58 -3280.139 95.99791 Q2 1999  £84,836.31 87,573.55 85,696.02 -859.7063 98.9968 Q3 1999  £92,744.56 92,198.75 89,886.15 +2858.41 103.18 Q4 1999  £94,032.88 97,293.15 94,745.95 -713.0687 99.24739 Q1 2000  £97,181.25 102,118.4 99,705.75 -2524.503 97.46805 Q2 2000  £105,213.9 105,675.6 103,897 +1316.93 101.2675 Q3 2000  £112,045.4 109,839.4 107,757.5 +4287.92 103.9792 Q4 2000  £108,261.8 113,161.1 111,500.3 -3238.45 97.09557 Q1 2001  £113,836.4 117,186.4 115,173.8 -1337.363 98.83883 Q2 2001  £118,500.9 123,428.2 120,307.3 -1806.388 98.49852 Q3 2001  £128,146.5 129,337.5 126,382.9 +1763.65 101.3955 Q4 2001  £133,228.9 136,672.2 133,004.8 +224.063 100.1685 Q1 2002  £137,473.8 145,696.3 141,184.2 -3710.4 97.37194 Q2 2002  £147,839.4 154,509.1 150,102.7 -2263.275 98.49218 Q3 2002  £164,242.9 163,239.4 158,874.3 +5368.64 103.3792 Q4 2002  £168,480.3 170,033.3 166,636.4 +1843.92 101.1066 Q1 2003  £172,395.1 174,140.6 172,087 +308.138 100.1791 Q2 2003  £175,015 178,801.1 176,470.9 -1455.85 99.17502 Q3 2003  £180,672 182,642.6 180,721.8 -49.825 99.97243 Q4 2003  £187,122.3 187,052.4 184,847.5 +2274.84 101.2307 Q1 2004  £187,760.9 192,088.1 189,570.3 -1809.35 99.04555 Q2 2004  £192,654.3 195,588.1 193,838.1 -1183.788 99.38929 Q3 2004  £200,815 198,330.8 196,959.4 +3855.56 101.9575 Q4 2004  £201,122 201,343.5 199,837.2 +1284.84 100.6429 Q1 2005  £198,732 203,390.8 202,367.1 -3635.138 98.20369 Q2 2005  £204,705 204,844.2 204,117.5 +587.5 100.2878 Q3 2005  £209,004.1 207,249.2 206,046.7 +2957.38 101.4353 Q4 2005  £206,935.8 209,719.3 208,484.3 -1548.45 99.25728 Q1 2006  £208,352 213,557.2 211,638.3 -3286.25 98.44723 Q2 2006  £214,585.2 218,996.8 216,277 -1691.825 99.21775 Q3 2006  £224,355.9 225,475.7 222,236.2 +2119.66 100.9538 Q4 2006  £228,694.2 232,444.5 228,960.1 -265.8625 99.88388 Q1 2007  £234,267.3 240,032.5 236,238.5 -1971.2 99.16559 Q2 2007  £242,460.5 246,276.9 243,154.7 -694.2125 99.7145 Q3 2007  £254,708.1 250,051.8 248,164.4 +6543.75 102.6369 Q4 2007  £253,671.7 251,542 250,796.9 +2874.83 101.1463 Q1 2008  £249,366.9 246,086.1 248,814 +552.887 100.2222 Q2 2008  £248,421.1 236,546.4 241,316.2 -7104.88 102.9442 Q3 2008  £232,884.6 226,903.8 231,725.1 +1159.5 100.5004 Q4 2008  £215,512.9 218,310.7 222,607.2 -7094.337 96.81307 Q1 2009  £210,796.7 216,358.6 217,334.6 -6537.9 96.99178 Q2 2009  £214,048.4 N/A N/A N/A N/A Q3 2009  £225,076.2 N/A N/A N/A N/A Q4 2009 N/A N/A N/A N/A N/A Q1 2010 N/A N/A N/A N/A N/A Average 2,177.91 99.9445% 4.2 Time Series Analysis on Inner London House Prices Year Price in GBP Moving Average Centered Average Seasonal Variation Seasonal Variation Multiplicative Model Q1 1996  £148,896.8 Q2 1996  £142,925.9 147,341.9 Q3 1996  £149,826.8 147,512.5 147,427.2 2,399.612 101.6277 Q4 1996  £147,718.2 154,229.6 150,871 -3,152.8 97.91027 Q1 1997  £149,578.9 163,204.5 158,717 -9,138.13 94.2425 Q2 1997  £169,794.3 171,798.4 167,501.5 2,292.85 101.3689 Q3 1997  £185,726.6 182,167.5 176,982.9 8,743.675 104.9404 Q4 1997  £182,093.8 189,947.1 186,057.3 -3,963.48 97.86976 Q1 1998  £191,055.1 194,649.2 192,298.1 -1,243.02 99.35359 Q2 1998  £200,912.9 195,698.1 195,173.6 5,739.3 102.9406 Q3 1998  £204,534.8 199,057.7 197,377.9 7,156.925 103.626 Q4 1998  £186,289.4 203,921.1 201,489.4 -15,200 92.45617 Q1 1999  £204,493.7 214,261.3 209,091.2 -4,597.52 97.80119 Q2 1999  £220,366.6 231,982.3 223,121.8 -2,755.21 98.76515 Q3 1999  £245,895.6 248,538.2 240,260.3 5,635.337 102.3455 Q4 1999  £257,173.3 263,644.1 256,091.1 1,082.162 100.4226 Q1 2000  £270,717.4 273,068.9 268,356.5 2,360.925 100.8798 Q2 2000  £280,789.9 282,179.8 277,624.4 3,165.55 101.1402 Q3 2000  £283,595 288,329.1 285,254.5 -1,659.46 99.41825 Q4 2000  £293,616.9 292,479.6 290,404.3 3,212.563 101.1062 Q1 2001  £295,314.7 301,948 297,213.8 -1,899.08 99.36104 Q2 2001  £297,391.6 302,959.7 302,453.9 -5,062.26 98.32627 Q3 2001  £321,468.8 307,248.3 305,104 16,364.8 105.3637 Q4 2001  £297,663.8 321,073.8 314,161 -16,497.2 94.7488 Q1 2002  £312,468.9 328,782.1 324,927.9 -12,459 96.1656 Q2 2002  £352,693.6 341,841 335,311.6 17,382.04 105.1838 Q3 2002  £352,302.1 346,899.1 344,370.1 7,932.025 102.3033 Q4 2002  £349,899.5 346,678.6 346,788.9 3,110.65 100.897 Q1 2003  £332,701.3 347,625.3 347,152 -14,450.7 95.83737 Q2 2003  £351,811.4 350,678.9 349,152.1 2,659.3 100.7616 Q3 2003  £356,089.1 358,687.6 354,683.2 1,405.862 100.3964 Q4 2003  £362,113.7 369,487.9 364,087.8 -1,974.05 99.45781 Q1 2004  £364,736.2 381,333.6 375,410.8 -10,674.6 97.15656 Q2 2004  £395,012.6 381,712.7 381,523.2 13,489.44 103.5357 Q3 2004  £403,472 390,406.4 386,059.6 17,412.45 104.5103 Q4 2004  £363,630 395,557 392,981.7 -29,351.7 92.53103 Q1 2005  £399,511 403,120 399,338.5 172.525 100.0432 Q2 2005  £415,615 413,411.6 408,265.8 7,349.25 101.8001 Q3 2005  £433,723.8 421,405.6 417,408.6 16,315.25 103.9087 Q4 2005  £404,796.4 431,011.5 426,208.5 -21,412.1 94.97614 Q1 2006  £431,487 441,305.4 436,158.4 -4,671.41 98.92896 Q2 2006  £454,038.7 450,973.4 446,139.4 7,899.35 101.7706 Q3 2006  £474,899.3 466,815.3 458,894.3 16,004.98 103.4877 Q4 2006  £443,468.4 485,594.8 476,205.1 -32,736.7 93.12551 Q1 2007  £494,854.8 510,784.7 498,189.8 -3,334.95 99.33059 Q2 2007  £529,156.8 533,983 522,383.8 6,772.975 101.2966 Q3 2007  £575,658.7 556,445.9 545,214.4 30,444.27 105.5839 Q4 2007  £536,261.6 566,853.2 561,649.5 -25,387.9 95.47975 Q1 2008  £584,706.4 568,963.1 567,908.2 16,798.24 102.9579 Q2 2008  £570,786.1 558,468.3 563,715.7 7,070.388 101.2542 Q3 2008  £584,098.4 553,527 555,997.6 28,100.76 105.0541 Q4 2008  £494,282.3 531,454.4 542,490.7 -48,208.4 91.11351 Q1 2009  £564,941.1 528,003.7 529,729.1 35,212.05 106.6472 Q2 2009  £482,495.8 N/A N/A, N/A N/A Q3 2009  £570,295.6 N/A N/A N/A N/A Q4 2009 N/A N/A N/A N/A N/A Q1 2010 N/A N/A N/A N/A N/A Average 11,049.32 100.0296% 4.3 Time Series Analysis on Number of House Sales Transactions in the UK Year Number of Transactions Moving Average Centered Average Seasonal Variation Seasonal Variation Multiplicative Model Q1 1990 344 Q2 1990 360 349.5 Q3 1990 349 342 345.75 3.25 100.94 Q4 1990 345 333 337.5 7.5 102.2222 Q1 1991 314 334 333.5 -19.5 94.15292 Q2 1991 324 326.5 330.25 -6.25 98.10749 Q3 1991 353 307 316.75 36.25 111.4444 Q4 1991 315 292.25 299.625 15.375 105.1314 Q1 1992 236 295.75 294 -58 80.27211 Q2 1992 265 284 289.875 -24.875 91.41871 Q3 1992 367 284.75 284.375 82.625 129.0549 Q4 1992 268 288.25 286.5 -18.5 93.54276 Q1 1993 239 283.25 285.75 -46.75 83.63955 Q2 1993 279 299 291.125 -12.125 95.83512 Q3 1993 347 314 306.5 40.5 113.2137 Q4 1993 331 322.25 318.125 12.875 104.0472 Q1 1994 299 321.75 322 -23 92.85714 Q2 1994 312 318.75 320.25 -8.25 97.42389 Q3 1994 345 315 316.875 28.125 108.8757 Q4 1994 319 307 311 8 102.5723 Q1 1995 284 295.25 301.125 -17.125 94.31299 Q2 1995 280 283.5 289.375 -9.375 96.76026 Q3 1995 298 277.75 280.625 17.375 106.1915 Q4 1995 272 278.75 278.25 -6.25 97.75382 Q1 1996 261 288.75 283.75 -22.75 91.98238 Q2 1996 284 310.75 299.75 -15.75 94.74562 Q3 1996 338 326.5 318.625 19.375 106.0808 Q4 1996 360 343.25 334.875 25.125 107.5028 Q1 1997 324 355.5 349.375 -25.375 92.73703 Q2 1997 351 359.75 357.625 -6.625 98.1475 Q3 1997 387 358 358.875 28.125 107.837 Q4 1997 377 349.5 353.75 23.25 106.5724 Q1 1998 317 347 348.25 -31.25 91.02656 Q2 1998 317 336.5 341.75 -24.75 92.75786 Q3 1998 377 336.25 336.375 40.625 112.0773 Q4 1998 335 342.5 339.375 -4.375 98.71087 Q1 1999 316 351.75 347.125 -31.125 91.03349 Q2 1999 342 367.25 359.5 -17.5 95.13213 Q3 1999 414 380 373.625 40.375 110.8063 Q4 1999 397 381.5 380.75 16.25 104.2679 Q1 2000 367 372.75 377.125 -10.125 97.31521 Q2 2000 348 358.25 365.5 -17.5 95.21204 Q3 2000 379 348.25 353.25 25.75 107.2895 Q4 2000 339 348 348.125 -9.125 97.37882 Q1 2001 327 352.25 350.125 -23.125 93.39522 Q2 2001 347 364.25 358.25 -11.25 96.85973 Q3 2001 396 368 366.125 29.875 108.1598 Q4 2001 387 380 374 13 103.4759 Q1 2002 342 395.25 387.625 -45.625 88.2296 Q2 2002 395 396.5 395.875 -0.875 99.77897 Q3 2002 457 396 396.25 60.75 115.3312 Q4 2002 392 373.75 384.875 7.125 101.8513 Q1 2003 340 349 361.375 -21.375 94.08509 Q2 2003 306 336 342.5 -36.5 89.34307 Q3 2003 358 362.75 349.375 8.625 102.4687 Q4 2003 340 399.25 381 -41 89.23885 Q1 2004 447 433.25 416.25 30.75 107.3874 Q2 2004 452 447.75 440.5 11.5 99.99119 Q3 2004 494 411 429.375 64.625 115.0509 Q4 2004 398 385.5 398.25 -0.25 99.93723 Q1 2005 300 373.25 379.375 -79.375 79.07743 Q2 2005 350 380.75 377 -27 99.85423 Q3 2005 445 403.5 392.125 52.875 113.4842 Q4 2005 428 422.5 413 15 103.632 Q1 2006 391 432.75 427.625 -36.625 91.43525 Q2 2006 426 444.25 438.5 -12.5 99.96777 Q3 2006 486 453 448.625 37.375 108.331 Q4 2006 474 458.25 455.625 18.375 104.0329 Q1 2007 426 459.25 458.75 -32.75 92.86104 Q2 2007 447 448.5 453.875 -6.875 100.0919 Q3 2007 490 N/A N/A N/A N/A Q4 2007 431 N/A N/A N/A N/A Average 24.44118 99.9445% 4.3 Regression Number of Transactions in the UK and Inner London House Prices Year Ended Number of Transactions UK (X) Median Inner London House Prices (Y) YX X ² Y ² Q1 1996 261  £148,896.8 38862064.8 68121 22170257050 Q2 1996 284  £142,925.9 40590955.6 80656 20427812891 Q3 1996 338  £149,826.8 50641458.4 114244 22448069998 Q4 1996 360  £147,718.2 53178552 129600 21820666611 Q1 1997 324  £149,578.9 48463563.6 104976 22373847325 Q2 1997 351  £169,794.3 59597799.3 123201 28830104312 Q3 1997 387  £185,726.6 71876194.2 149769 34494369948 Q4 1997 377  £182,093.8 68649362.6 142129 33158151998 Q1 1998 317  £191,055.1 60564466.7 100489 36502051236 Q2 1998 317  £200,912.9 63689389.3 100489 40365993386 Q3 1998 377  £204,534.8 77109619.6 142129 41834484411 Q4 1998 335  £186,289.4 62406949 112225 34703740552 Q1 1999 316  £204,493.7 64620009.2 99856 41817673340 Q2 1999 342  £220,366.6 75365377.2 116964 48561438396 Q3 1999 414  £245,895.6 101800778.4 171396 60464646099 Q4 1999 397  £257,173.3 102097800.1 157609 66138106233 Q1 2000 367  £270,717.4 99353285.8 134689 73287910663 Q2 2000 348  £280,789.9 97714885.2 121104 78842967942 Q3 2000 379  £283,595 107482505 143641 80426124025 Q4 2000 339  £293,616.9 99536129.1 114921 86210883966 Q1 2001 327  £295,314.7 96567906.9 106929 87210772036 Q2 2001 347  £297,391.6 103194885.2 120409 88441763751 Q3 2001 396  £321,468.8 127301644.8 156816 1.03342E+11 Q4 2001 387  £297,663.8 115195890.6 149769 88603737830 Q1 2002 342  £312,468.9 106864363.8 116964 97636813467 Q2 2002 395  £352,693.6 139313972 156025 1.24393E+11 Q3 2002 457  £352,302.1 161002059.7 208849 1.24117E+11 Q4 2002 392  £349,899.5 137160604 153664 1.2243E+11 Q1 2003 340  £332,701.3 113118442 115600 1.1069E+11 Q2 2003 306  £351,811.4 107654288.4 93636 1.23771E+11 Q3 2003 358  £356,089.1 127479897.8 128164 1.26799E+11 Q4 2003 340  £362,113.7 123118658 115600 1.31126E+11 Q1 2004 447  £364,736.2 163037081.4 199809 1.33032E+11 Q2 2004 452  £395,012.6 178545695.2 204304 1.56035E+11 Q3 2004 494  £403,472 199315168 244036 1.6279E+11 Q4 2004 398  £363,630 144724740 158404 1.32227E+11 Q1 2005 300  £399,511 119853300 90000 1.59609E+11 Q2 2005 350  £415,615 145465250 122500 1.72736E+11 Q3 2005 445  £433,723.8 193007091 198025 1.88116E+11 Q4 2005 428  £404,796.4 173252859.2 183184 1.6386E+11 Q1 2006 391  £431,487 168711417 152881 1.86181E+11 Q2 2006 426  £454,038.7 193420486.2 181476 2.06151E+11 Q3 2006 486  £474,899.3 230801059.8 236196 2.25529E+11 Q4 2006 474  £443,468.4 210204021.6 224676 1.96664E+11 Q1 2007 426  £494,854.8 210808144.8 181476 2.44881E+11 Q2 2007 447  £529,156.8 236533089.6 199809 2.80007E+11 Q3 2007 490  £575,658.7 282072763 240100 3.31383E+11 Q4 2007 431  £536,261.6 231128749.6 185761 2.87577E+11 SUM 18,202  £15,218,243 5,982,454,675 7,053,270 5.45022E+12 Regression Line b = 1,401.870047 a = -214,554.0571 y = -214,554 + 1,401.87x Correlation Coefficient n = 48, à ¢Ã‹â€ Ã¢â‚¬ËœXY = 5,982,454,675 à ¢Ã‹â€ Ã¢â‚¬ËœX = 18,202 à ¢Ã‹â€ Ã¢â‚¬ËœY = 15,218,243 à ¢Ã‹â€ Ã¢â‚¬ËœX ² = 7,053,270 à ¢Ã‹â€ Ã¢â‚¬ËœY ² = 5,450,220,000,000 Therefore; r = 0.6886964582 Coefficient of Determination r ² = 0.4743028115 4.4 Regression Number of Transactions in the UK and Outer London House Prices Year Ended Number of Transactions UK (X) Median Outer London House Prices (Y) YX X ² Y ² Q1 1996 261  £59,562.61 15545841.21 68121 3547704510 Q2 1996 284  £57,936.38 16453931.92 80656 3356624128 Q3 1996 338  £61,673.3 20845575.4 114244 3803595933 Q4 1996 360  £61,919.44 22290998.4 129600 3834017050 Q1 1997 324  £61,595.91 19957074.84 104976 3794056129 Q2 1997 351  £65,902.75 23131865.25 123201 4343172458 Q3 1997 387  £69,763.31 26998400.97 149769 4866919422 Q4 1997 377  £69,279.5 26118371.5 142129 4799649120 Q1 1998 317  £72,235.88 22898773.96 100489 5218022359 Q2 1998 317  £77,777.06 24655328.02 100489 6049271062 Q3 1998 377  £77,881.31 29361253.87 142129 6065498447 Q4 1998 335  £79,012.63 26469231.05 112225 6242995700 Q1 1999 316  £78,680.44 24863019.04 99856 6190611639 Q2 1999 342  £84,836.31 29014018.02 116964 7197199494 Q3 1999 414  £92,744.56 38396247.84 171396 8601553410 Q4 1999 397  £94,032.88 37331053.36 157609 8842182521 Q1 2000 367  £97,181.25 35665518.75 134689 9444195352 Q2 2000 348  £105,213.9 36614437.2 121104 11069964753 Q3 2000 379  £112,045.4 42465206.6 143641 12554171661 Q4 2000 339  £108,261.8 36700750.2 114921 11720617339 Q1 2001 327  £113,836.4 37224502.8 106929 12958725965 Q2 2001 347  £118,500.9 41119812.3 120409 14042463301 Q3 2001 396  £128,146.5 50746014 156816 16421525462 Q4 2001 387  £133,228.9 51559584.3 149769 17749939795 Q1 2002 342  £137,473.8 47016039.6 116964 18899045686 Q2 2002 395  £147,839.4 58396563 156025 21856488192 Q3 2002 457  £164,242.9 75059005.3 208849 26975730200 Q4 2002 392  £168,480.3 66044277.6 153664 28385611488 Q1 2003 340  £172,395.1 58614334 115600 29720070504 Q2 2003 306  £175,015 53554590 93636 30630250225 Q3 2003 358  £180,672 64680576 128164 32642371584 Q4 2003 340  £187,122.3 63621582 115600 35014755157 Q1 2004 447  £187,760.9 83929122.3 199809 35254155569 Q2 2004 452  £192,654.3 87079743.6 204304 37115679308 Q3 2004 494  £200,815 99202610 244036 40326664225 Q4 2004 398  £201,122 80046556 158404 40450058884 Q1 2005 300  £198,732 59619600 90000 39494407824 Q2 2005 350  £204,705 71646750 122500 41904137025 Q3 2005 445  £209,004.1 93006824.5 198025 43682713817 Q4 2005 428  £206,935.8 88568522.4 183184 42822425322 Q1 2006 391  £208,352 81465632 152881 43410555904 Q2 2006 426  £214,585.2 91413295.2 181476 46046808059 Q3 2006 486  £224,355.9 109036967.4 236196 50335569865 Q4 2006 474  £228,694.2 108401050.8 224676 52301037114 Q1 2007 426  £234,267.3 99797869.8 181476 54881167849 Q2 2007 447  £242,460.5 108379843.5 199809 58787094060 Q3 2007 490  £254,708.1 124806969 240100 64876216206 Q4 2007 431  £253,671.7 109332502.7 185761 64349331381 SUM 18,202  £6,877,314 2,719,147,638 7,053,270 1.17288E+12 Regression Line b = 736.8998122 a = -136,161.1746 y = -136,161.2 + 736.9 x Correlation Coefficient n = 48, à ¢Ã‹â€ Ã¢â‚¬ËœXY = 2,719,147,638 à ¢Ã‹â€ Ã¢â‚¬ËœX = 18,202 à ¢Ã‹â€ Ã¢â‚¬ËœY = 6,877,314 à ¢Ã‹â€ Ã¢â‚¬ËœX ² = 7,053,270 à ¢Ã‹â€ Ã¢â‚¬ËœY ² = 1,172,880,000,000 Therefore; r = 0.6610921139 Coefficient of Determination r ² = 0.4370427831 4.4 Regression Number of Transactions in the UK and Outer London House Prices Year Ended Median Inner London House Prices Median Outer London House Prices (Y) YX X ² Y ² Q1 1996  £148,896.8  £59,562.61 8868682029 22170257050 3547704510 Q2 1996  £142,925.9  £57,936.38 8280609254 20427812891 3356624128 Q3 1996  £149,826.8  £61,673.3 9240313184 22448069998 3803595933 Q4 1996  £147,718.2  £61,919.44 9146628222 21820666611 3834017050 Q1 1997  £149,578.9  £61,595.91 9213448462 22373847325 3794056129 Q2 1997  £169,794.3  £65,902.75 11189911304 28830104312 4343172458 Q3 1997  £185,726.6  £69,763.31 12956902371 34494369948 4866919422 Q4 1997  £182,093.8  £69,279.5 12615367417 33158151998 4799649120 Q1 1998  £191,055.1  £72,235.88 13801033277 36502051236 5218022359 Q2 1998  £200,912.9  £77,777.06 15626414678 40365993386 6049271062 Q3 1998  £204,534.8  £77,881.31 15929438165 41834484411 6065498447 Q4 1998  £186,289.4  £79,012.63 14719215435 34703740552 6242995700 Q1 1999  £204,493.7  £78,680.44 16089654293 41817673340 6190611639 Q2 1999  £220,366.6  £84,836.31 18695089191 48561438396 7197199494 Q3 1999  £245,895.6  £92,744.56 22805479228 60464646099 8601553410 Q4 1999  £257,173.3  £94,032.88 24182746058 66138106233 8842182521 Q1 2000  £270,717.4  £97,181.25 26308655329 73287910663 9444195352 Q2 2000  £280,789.9  £105,213.9 29543000460 78842967942 11069964753 Q3 2000  £283,595  £112,045.4 31775515213 80426124025 12554171661 Q4 2000  £293,616.9  £108,261.8 31787494104 86210883966 11720617339 Q1 2001  £295,314.7  £113,836.4 33617562315 87210772036 12958725965 Q2 2001  £297,391.6  £118,500.9 35241172252 88441763751 14042463301 Q3 2001  £321,468.8  £128,146.5 41195101579 1.03342E+11 16421525462 Q4 2001  £297,663.8  £133,228.9 39657420644 88603737830 17749939795 Q1 2002  £312,468.9  £137,473.8 42956287065 97636813467 18899045686 Q2 2002  £352,693.6  £147,839.4 52142010208 1.24393E+11 21856488192 Q3 2002  £352,302.1  £164,242.9 57863118580 1.24117E+11 26975730200 Q4 2002  £349,899.5  £168,480.3 58951172730 1.2243E+11 28385611488 Q1 2003  £332,701.3  £172,395.1 57356073884 1.1069E+11 29720070504 Q2 2003  £351,811.4  £175,015 61572272171 1.23771E+11 30630250225 Q3 2003  £356,089.1  £180,672 64335329875 1.26799E+11 32642371584 Q4 2003  £362,113.7  £187,122.3 67759548406 1.31126E+11 35014755157 Q1 2004  £364,736.2  £187,760.9 68483197175 1.33032E+11 35254155569 Q2 2004  £395,012.6  £192,654.3 76100875944 1.56035E+11 37115679308 Q3 2004  £403,472  £200,815 81023229680 1.6279E+11 40326664225 Q4 2004  £363,630  £201,122 73133992860 1.32227E+11 40450058884 Q1 2005  £399,511  £198,732 79395620052 1.59609E+11 39494407824 Q2 2005  £415,615  £204,705 85078468575 1.72736E+11 41904137025 Q3 2005  £433,723.8  £209,004.1 90650052468 1.88116E+11 43682713817 Q4 2005  £404,796.4  £206,935.8 83766866871 1.6386E+11 42822425322 Q1 2006  £431,487  £208,352 89901179424 1.86181E+11 43410555904 Q2 2006  £454,038.7  £214,585.2 97429985247 2.06151E+11 46046808059 Q3 2006  £474,899.3  £224,355.9 1.06546E+11 2.25529E+11 50335569865 Q4 2006  £443,468.4  £228,694.2 1.01419E+11 1.96664E+11 52301037114 Q1 2007  £494,854.8  £234,267.3 1.15928E+11 2.44881E+11 54881167849 Q2 2007  £529,156.8  £242,460.5 1.283E+11 2.80007E+11 58787094060 Q3 2007  £575,658.7  £254,708.1 1.46625E+11 3.31383E+11 64876216206 Q4 2007  £536,261.6  £253,671.7 1.36034E+11 2.87577E+11 64349331381 Q1 2008  £584,706.4  £249,366.9 1.45806E+11 3.41882E+11 62183850816 Q2 2008  £570,786.1  £248,421.1 1.41795E+11 3.25797E+11 61713042925 Q3 2008  £584,098.4  £232,884.6 1.36028E+11 3.41171E+11 54235236917 Q4 2008  £494,282.3  £215,512.9 1.06524E+11 2.44315E+11 46445810066 Q1 2009  £564,941.1  £210,796.7 1.19088E+11 3.19158E+11 44435248731 Q2 2009  £482,495.8  £214,048.4 1.03277E+11 2.32802E+11 45816717543 Q3 2009  £570,295.6  £225,076.2 1.2836E+11 3.25237E+11 50659295806 SUM  £19,069,848  £8,473,421 3.39612E+12 7.58058E+12 1.53837E+12 Regression Line b = 736.8998122 a = -136,161.1746 y = -136,161.2 + 736.9 x Correlation Coefficient n = 55, à ¢Ã‹â€ Ã¢â‚¬ËœXY = 3,396,120,000,000 à ¢Ã‹â€ Ã¢â‚¬ËœX = 19,069,848 à ¢Ã‹â€ Ã¢â‚¬ËœY = 8,473,421 à ¢Ã‹â€ Ã¢â‚¬ËœX ² = 7,580,580,000,000 à ¢Ã‹â€ Ã¢â‚¬ËœY ² = 1,538,370,000,000 Therefore; r = 0.4730322188 Coefficient of Determination r ² = 0.22375948 Chapter 5: Conclusion Recommendations