Home Value and Yearly Expenditure on Home Maintenance - Dissertation Example

Home Value and Yearly Expenditure on Home Maintenance Abstract The factors which affect the maintenance expenditure of a home is linked with features like the location of the site in relation to employment centers and other recreational facilities (accessibility) and physical characteristics of property. Apart from this, the social and economical features like the neighborhood, the presence of facilities like schools, parks, community services and other views. The State and Local governments normally provide these attributes. This paper examines the relationship between home value and yearly expenditure on home maintenance in Hong Kong. The analysis of the results indicates that different attributes are evaluated differently which in turn determines the value of homes. In addition, a comparative study is made with respect to the maintenance cost of homes. The results from the study conducted indicate that the value of a home in Hong Kong largely depends on its annual maintenance expenditure. 1. Introduction Property is a product, which has many dimensions. The quantity and character of weights on the apparent value of property are big and quite a few. The impact of the location of the property also adjudicates its value. Physical location denotes the situation of one site in relation to another. The term physical location interchangeably denotes nearness and convenience (Fanning and Stephen, 1994). Location relates to a range of conveniences like the accessibility to shopping centre, educational and leisure facilities etc. Real property is a key asset class that finds a place in many investor portfolios. Many investigators are working for “pricing” models for this class of assets. Hedonic regressions are an accepted method in the development of real estate valuation models (pricing models). In these models, a number of variables, which relate to the property, are included in a hedonic function of the form: P = f({L}, {S}, {N}) Where {L} is a set of locational attributes, {S} is a set of structural attributes and {N} is a set of neighbourhood attributes. (Fehribach, Rutherford and Eakin, 1993). Numerous studies made use of this technique to prove the relationship between attribute inclination and the price of properties (Gillard, 1981; Li & Brown, 1980; Sirpal, 1994; Walden, 1990). The buyers’ evaluations of the housing unit’s bundle of inherent attributes, such as location, structure, or neighborhood attributes helps in fixing the market price of a housing unit (Freeman, 1979). A few were conducted in the East, such as in Hong Kong (Chau, Ma, & Ho, 2001; Chau, Ng, & Hung, 2001; Mok, 1995; Mok, Chan, & Cho, 1995; So, Tse, & Ganesan, 1996). Pollakowski (1982) points out that fixing of house values not only depend on approachability but it also depends on ecological attributes of the location. The ecological attributes like the neighborhood amenity, parks, and security are the deciding factors of the value of a property. The impact and authority of the several attributes automatically exercise multifaceted dealings, which in turn affects the value of properties. This contributes to households that react to differential public service degrees by transmigrating between communities to conform to their wants. The housing sector is immensely related to the economic health and wealth of a nation. A heavy demand for housing will most likely generate growth in several other economic sectors. Thus, study of the variables that affect property prices is indispensable as the procurement of a residential property is both a savings decision as well as an expenditure decision. In the attempt to model the housing prices, two methods have been extensively used. The first mode of property valuation is the monocentric model, where housing price is unspecified and is a function of closeness to a workplace. The second model is that which it has been shown that the property value depends on the factors of maintenance expenditure in a year. As a result, although the majority of the studies conducted have used locational and political boundaries, or the demographic and socioeconomic characteristics of households such as race and income the area of quality of structure of the homes has not been examined at all. The quality of the structure of a home is directly linked to its annual maintenance expenditure. The final proportional price of a house is the addition of all its marginal or absolute prices figured by the regression analysis. As Rosen (1974) integrated income right away in the budget restraint of the consumer, when income increments, the consumer’s marginal temperament to pay for a certain inherent attribute may also alter. It is accepted that the buyer’s demand price or temperament to pay for a trait is a function of the utility level, the purchaser’s income, and other variables, which regulate tastes and preferences, such as age, education, and so on. 2. Literature review 2.1 Various Studies A number of investigators have consistently probed the impact of neighborhood and location attributes on the measure of properties. For example, Modeling The Influence of Location on Value (Gallimore, et al, 1996); The Value Impact of New Residential Construction and Neighborhood Disinvestment on Residential Sales Price (Simons, Quercia, and Maric, 1998) and Empirical Study of the Relationship between Residential Lot Price, Size and View (Marvin, 1997) all have been studied. It is in general accepted that locative housing choice is predominately the consequence of the impact of different neighborhood and government externalities on households. Residential properties are multidimensional goods qualified by enduringness, structural rigidness, and spatial fixedness (Chau et al., 2001; So et al., 1996). Normally the housing properties are sorted into locational attributes (L), structural attributes (S), and neighborhood attributes (N). These properties cover both quantitative and qualitative attributes (Goodman, 1989; Williams, 1991). The market prices (P) of the property can, therefore, be expressed as: P = f (L, S, N) (1) The partial derivative of the supra hedonic function with regard to any characteristics is the implied marginal attribute price, ceteris paribus (Rosen, 1974). This implied price of the housing attribute is disclosed in the regression coefficient. Lancaster’s (1980) theory presumes a linear relationship between the price of goods and the features contained in those goods. embedded prices are stable over ranges of characteristic amounts. They vary when there is an alteration in the combination of goods consumed. Similar to Lancaster’s study our study of house price and maintenance expenditure differs with the attributes linked to such houses. 2.2 Locational Attributes The location of a property has been considered in virtually all studies undertaken in terms of permanent and proportional locational attributes. The fixed locational attributes (Follain & Jimenez, 1985; Orford, 1988) are measured with regard to the entire urban area, and relate to some type of approachability measure. According to Dubin & Sung, (1990, pp. 97-110) Relative locational attributes are measured through alternate measures like the socio-economic class, aesthetic attributes, pollution levels, racial composition and proximity to local amenities. In the conventional analysis of location, approachability is evaluated in terms of approach to the Central Business District (CBD). What ever form of measurement is used for accessibility, it shows that this attribute has some authority on housing prices (McMillan, Jarmin, & Thorsnes, 1992; Palmquist, 1992; Ridker & Henning, 1968). Transport availability is often linked with the ease of commuting to and from facilities, and is calculated by cost of travel, travelling time, convenience, and accessibility of different transport modes (Adair, Greal, Smyth, Cooper, & Ryley, 2000; So et al., 1996). Purchasers have a tendency to exchange housing costs against transport costs, but according to Edmonds (1984) this is not always true as he discovered that costs of commuting (fares) may not be capitalised with site value. The optimistic power of good public transport services on housing prices has been examined by through empirical observation. So et al.’s (1996) work in Hong Kong on transport convenience, considered by the distances to the nearby stations of the mass transit railway (MTR), minibuses, and buses disclosed a high reliance on public transport in the territory. Benson, et al (1998) also mentioned that the view agreeableness might not be consistent; it changes by type (for instance water view, Mountain View, and valley view) and by quality (for instance full view, partial view or poor partial view). Assorting the views as oceanfront, ocean view, partial ocean view, and no view, they revealed that, comparative to no view, an ocean frontage adds 147 % to a property’s selling price, an ocean view adds 32 %, and a partial ocean view adds 10 %. 2.3 Structural Attributes Costs of properties are often linked to their structural attributes. As Ball (1973) stated that if a house had more advantageous properties than others, the rating of these attributes would be reverberated in higher market prices for this house. On the other hand, other researchers have mentioned that structural attributes opted by purchasers may not always be the same. Kohlhase (1991) detected that the implication of structural attributes can alter over time, and may deviate between nations. Chau et al. (2001) relegated the physical considerations of the property such as size, age, floor level, etc., as tangible attributes, while properties like ease of access, sea view, ecological quality, and builder’s good will are considered as intangible attributes. According to Chau et al., purchasers are eager to pay about HK$416 more per square foot for properties built by big prestigious builders. This is roughly 7% more than average housing prices. 2.4 Neighbourhood Attributes Goodman (1989) indicated that while neighbourhood properties cannot be openly calculated in the market place, they could be completely calculated through hedonic pricing by equating houses with disagreeing neighbourhood qualities. Goodman’s caution that failure to consider neighbourhood attributes may result in substantial errors when evaluating individual properties. The market in general, was corroborated by Linneman (1980). Linneman determined that between 15% and 50% of the interchangeable difference in site ratings is assigned to neighbourhood attributes. But for sites with similar structures, almost 100% of the differential in site evaluations is caused by neighbourhood attributes. Kain and Quigley’s (1970) research further manifested that, upper income households who are more educated desire to live in comparatively high quality housing units situated further away from the CBD. 2.5 Repairs for an existing home Home maintenance expenses do not represent vaguely to alterations conducted on the quality of a home since quality has both vertical and horizontal elements. For instance, painting a room with a favorite color may insert nothing to the marketing value of the home, but by fixing, a pipe that leaks almost positively raises resale value Gyourko and Tracy (2003). Further, houses that need maintenance to a lesser extent are more worthy than those, which need more, all else equal amount of investment in repairs. Hence straight regressing housing quality standards on home maintenance expenses would not give an excellent idea of the results of home maintenance for housing quality. One might disagree that a desire to settle in place, defensive savings needs, the structure of Medicaid welfares, and lack of a legacy motive could reduce the under maintenance. However, a homeowner will not wish to remain in place for a long time with out any bequest motive Rodda et al. (2000). 3. Case study and Analysis For the purpose of analysis, data of 40 house values and maintenance expenditure was collected. The following analysis was worked out. The value of homes and their maintenance expenditure are calculated thus (Table 1 & Table 2): Table 1 showing Value of Homes N Valid 40 Missing 0 Mean 1744885.00 Median 1741400.00 Std. Deviation 566029.349 Minimum 489000 Maximum 2861800 Percentiles 25 1274750.00 50 1741400.00 75 2236650.00 Table 2 Showing Maintenance Expenditure N Valid 40 Missing 0 Mean 9180.9400 Median 8708.7000 Std. Deviation 4356.75607 Minimum 1254.00 Maximum 20106.40 Percentiles 25 6083.3000 50 8708.7000 75 13074.9500 Table 3 showing the correlations calculated Correlations Value of Home Maintenance Expenditure Value of Home Pearson Correlation 1 .987** Sig. (2-tailed) .000 N 40 40 Maintenance Expenditure Pearson Correlation .987** 1 Sig. (2-tailed) .000 N 40 40 **. Correlation is significant at the 0.01 level (2-tailed). We can clearly infer that with Pearson Correlation when the value of home is at 1 then maintenance expenditure is below 1, which only means that maintenance expenditure of a house is not too much. The above graph shows that with the increase in the cost of homes their expenditure on maintenance also increases. It proves that a direct relationship exists between value of homes and their annual maintenance expenses. Classified Maintenance Expenditure Frequency Percent Valid Percent Cumulative Percent Valid 1-2500 1 2.5 2.5 2.5 2501-5000 8 20.0 20.0 22.5 5001-7500 5 12.5 12.5 35.0 7501-10000 11 27.5 27.5 62.5 10001-12500 4 10.0 10.0 72.5 12501-15000 8 20.0 20.0 92.5 15001-17500 2 5.0 5.0 97.5 20001-22500 1 2.5 2.5 100.0 Total 40 100.0 100.0 The analysis also proved the study by Cheung et al. (1995) with regard to the relationship between house value and maintenance expenditure is precious in three particular facets aspects. First, it constitutes the first attempt that centers on the relationship between property value and maintenance expenditure in Hong Kong’s property market. Second, it renders a different way to find the connections among variables that evaluate property values. Furthermore, their outcome that property value alters due to changes in maintenance expenditure is interesting, as it proposes the two expenses are alternates in nature. Finally, according to Blackley and Follain (1996), it is almost certainly easier to recognize a connection between housing expenditure and user cost in Hong Kong. The user cost of owning a home, which is the annual maintenance expenditure, can be presented in the form of a function of the interest and opportunity costs of housing equity, the anticipated increase of property value, and other housing expenditures, which includes property tax, depreciation and maintenance. The user cost of housing services per unit expense, uc, is thus defined as: uc = i + T + d – g, (4); where i is the mortgage interest rate, T is the rates assessed on the property value, d is the sum of maintenance and depreciation cost, and g is the expected capital gains. On the other hand, as described in Cheung et al. (1995), the proof that a fundamental relationship exists between property value and yearly maintenance expenditure is not persistent to all cases of housing units. Causal relationships are noticed primarily in flats with bigger floor areas, which amount to just around 5% of all residential units. The lack of a connecting relationship in most examples is the result of division in the housing market (owner-occupied and rental markets), it is also probable that the effort to institute a basic relationship between property value and maintenance cost is itself debatable. As analyzed with regression and charts, it can be notices that the alteration in user cost instead of the property value drives property owners to raise or lower rental price. If there is an alteration in the property value, it will first be reverberated in a change in the maintenance expenditure of the house. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. 1 (Constant) -4077.428 365.065 -11.169 .000 Value of Home .008 .000 .987 38.135 .000 a. Dependent Variable: Maintenance Expenditure All the coefficients were of the expected signs and were highly significant. The significance level of all coefficients was similar. The high value of the home price with a small number of independent variables indicates that the annual home maintenance expenditure are not very homogenous, which only shows that the higher the cost of homes the higher is the maintenance expenses. Based on the study a linear regression model is derieved as follows: ME = a + b1X1 + b2X2 + ………+ bnXn Where ME = Maintenance expenditure a and b1, b2…etc. are Variable factors and they can change the annual Maintenance Expenditure. The variable factor constantly changes as we have seen from the analysis made. The attribute of the variable factors are age of the owner of the house, area of the house property, price of the property etc. When ever there is an increase in the variable the maintenance expenditure also simultaneously increases. The user cost of owning a home, which is the annual maintenance expenditure, can be presented in the form of a function of the interest and opportunity costs of housing equity, the anticipated increase of property value, and other housing expenditures, which includes property tax, depreciation and maintenance. In addition, the following linear regression model can be derieved from the analysis and the case study made above: ME = a + b1X1 + b2X2 + …+ bnXn ME = Maintenance expenditure X = Variable factors such as X1 = Size of property X2 = Age of property owner Xn = floor level of property. Moreover, results from surveys conducted by real estate professionals to key out the elements viewed as important by buyers when purchasing properties in Hong Kong showed that buyers in Hong Kong do demonstrate tastes for certain attributes, tangible or otherwise. The attributes like proximity to the CBD, a bird's-eye view of the sea, freehold land tenure, good environmental attributes, big floor area, and so on. 4. Conclusions & Recommendations The primary objective of this research was to study the relationship between house prices and its maintenance costs in Hong Kong. The literature for the same was reviewed. Case study and analysis were presented with the help of graphs and tables. The complete pattern of predictable prices is fundamentally dependable with previous expectations. The results highlight the importance of physical attributes as principal determiners of the value of homes and the variable like the price of the property, the age of the owner and other attributes on which the maintenance expenditure of the house depends. In addition, the result also shows that the maintenance cost of a house increases with the age of the property. Such properties are not in much demand. In essence, the growth factors are the most vital factors disturbing the real quantity of residential land issued and not just the size of a piece of land given. Thus, it can be concluded that when less expenditure on houses are to be made the more demand such houses create among the investors. It is recommended that further research work can be done. Since the real property value increase is high, the user cost should be arrived at by taking into consideration all the factors that makes the maintenance cost. Since tax payment is also a maintenance cost, this gives an opportunity for further study. This paper presents some preliminary findings about the house value and their annual expenditure in Hong Kong’s residential property market. There are some areas where additional work can be done. The cost with regard to different attributes of a house like locational, structural etc., have a lot of scope for further research work. Very little study has being conducted in these areas. Structural quantity has been well explored but there has been relatively very less or no research at all conducted on structural quality. This area seems to be a good spot for future study. The study would bring into focus the cost of the house and its annual maintenance expenditure due to the structural quality of the building. Research with regard to structural quality was not undertaken because of the difficulty in measuring objectively and exactly the physical and environmental quality of the properties (Kain & Quigley, 1970; Morris, Woods, & Jacobson, 1972). In addition, not much research has been undertaken on cemetery views except by Tse and Love (2000), who discovered that a cemetery view has a negative affect on a property’s price in Hong Kong. This area also has a good scope for further research work to be carried out. This will also have an effect on the house prices and its maintenance cost. Hence, it can be conclude that IKHP in order to increase its profits will have to either open branches in places where it has no branch and should go in for more promotional activities. It has to reach the mass through mass media advertisements. Reference 1. Adair, A. S., Greal, S., Smyth, A, Cooper, J. & Ryley, T. (2000). House prices and accessibility: The testing of relationships within the Belfast urban area, Housing Studies, vol. 15, no. 5, pp. 699-716. 2. Ball, M. (1973). Recent empirical work of the determinants of relative house prices, Urban Studies, vol. 10, pp. 213-233. 3. Benson, E. D., Hansen, J. L., Schwartz, A. L. & Smersh, G. T. (1998). Pricing residential amenities: The value of a view, Journal of Real Estate Finance and Economics, vol. 16, no. 1, pp. 55-73. 4. Blackley, D. and J. R. Follain, In search of empirical evidence that links rent and user cost, Regional Science and Urban Economics, 26, 409–431, 1996. 5. Chau, K. W., Ma, V. S. M. & Ho, D. C. W. (2001). The pricing of “luckiness” in the apartment market, Journal of Real Estate Literature, vol. 9, no. 1, pp. 31-40. 6. Chau, K. W., Ng, F. F. & Hung, E. C. T. (2001). Developer's good will as significant influence on apartment unit prices, Appraisal Journal, vol. 69, pp. 26-34. 7. Cheung, Y., S. Tsang and S. Mak, The causal relationships between residential property prices and rentals in Hong Kong: 1982–1991, Journal of Real Estate Finance and Economics, 10, 23–35, 1995. 8. Dubin, R. A. & Sung, C. H. (1990). Specification of hedonic regressions: Non-nested tests on measures of neighbourhood quality, Journal of Urban Economics, vol. 27, pp. 97-110. 9. Edmonds, R. (1984). A theoretical basis for hedonic regression: A research primer, Areuea Journal, vol. 12, no. 1, pp. 72-85. 10. Fanning, Stephen, F. (1994), Market analysis for Valuation appraisals, Appraisal Institute. 11. Fehribach, F., Rutherford, R. and Eakin, M. (1993) An Analysis of the Determinants of Industrial Property Valuation, Journal of Real Estate Research, 8:3, pp.365-76. 12. Follain, J. R. & Jimenez, E. (1985), Estimating the demand for housing characteristics, Regional Science and Urban Economics, vol. 15, pp. 77-107. 13. Freeman, A. M. (1979). Hedonic prices, property values and measuring environmental benefits: A survey of the issues, Scandinavian Journal of Economics, vol. 81, pp. 154-171. 14. Gallimore, P., Fletcher, M., and Carter, M. (1996), “Modelling the influence of location on value”, Journal of Property Valuation & Investment, Vol. 14 No. 1, pp. 6-19. 15. Gillard, Q. (1981). The effect of environment amenities on house values: The example of a view lot, Professional Geographer, vol. 33, pp. 216-220. 16. Goodman, A. C. (1989). Topics in empirical urban housing research, The Economics of Housing Markets, in R. Muth & A. Goodman, Harwood Academic, Chur, Switzerland, pp. 49-146. 17. Gyourko, Joseph and Joseph Tracy, “Using Home Maintenance and Repairs to Smooth Consumption,” 2003. Working Paper, Wharton. 18. Kohlhase, J. E. (1991). The impact of toxic waste sites on housing values, Journal of Urban Economics, vol. 30, pp. 1-26. 19. Kain, J. F. & Quigley, J. M. (1970). Measuring the value of housing quality, Journal of the American Statistical Association, vol. 65, pp. 532-548. 20. Lancaster, K. J. (1966). A new approach to consumer theory, Journal of Political Economy, vol. 74, pp. 132-157. 21. Lancaster, Kelvin J. (1980) Intra-Industry Trade under Perfect Monopolistic Competition, Journal of International Economics, 10 (2), pp. 151-175. 22. Linneman, P. (1980). Some empirical results on the nature of the hedonic price function for the urban housing market, Journal of Urban Economics, vol. 8, no. 1, pp. 47 – 68. 23. Li, M. M. & Brown, H. J. (1980). Micro-neighbourhood externalities and hedonic housing prices, Land Economics, vol. 56, no. 2, pp. 125-141. 24. Marvin, L.W. (1997), “Empirical study of the relationship between residential lot price, size and view”, Journal of Property Valuation & Investment, Vol. 15 No. 1, pp. 48-57. 25. McMillan, D., Jarmin, R. & Thorsnes, P. (1992). Selection bias and land development in the monocentric model, Journal of Urban Economics, vol. 31, pp. 273-284. 26. Mok, H. M. K. (1995). A hedonic approach to pricing of residential properties in Hong Kong, Hong Kong Journal of Business Management, vol. 8, pp. 1-15. 27. Mok, H. M. K., Chan, P. P. K. & Cho, Y-S.(1995). A hedonic price model for private properties in Hong Kong, Journal of Real Estate Finance and Economics, vol. 10, pp.37-48. 28. Morris, E. W., Woods, M. E. & Jacobson, A. L. (1972). The measurement of housing quality, Land Economics, vol. 2, pp. 383-387. 29. Orford, S. (1988). Valuing location in an urban housing market, in the Proceedings of the 3rd International Conference on GeoComputation, United Kingdom, University of Bristol. 30. Palmquist, R. B. (1992). Valuing localized externalities, Journal of Urban Economics, vol. 31, pp. 59-68. 31. Pollakowski, H.O., (1982), Urban Housing Markets and Residential Location, D.C. Heath and Company, Lexington, MA. 32. Ridker, R. G. & Henning, J. A. (1968). The determination of residential property value with special reference to air pollution, Review of Economics and Statistics, vol. 49, pp. 246-257. 33. Rodda, David, Christopher Herbert, and Hin-Kin Lam, “Evaluation Report of FHA’s Home Equity Conversion Mortgage Insurance Demonstration,” Prepared for US Department of Housing and Urban Development, Abt Associates 2000. 34. Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition, Journal of Political Economy, vol. 82, no. 1, pp. 35-55. 35. Simons, R.A., Quercia, R.G., and Maric, I. (1998), “The Value Impact of New Residential Construction and Neighborhood Disinvestment on Residential Sales Price”, Journal of Real Estate Research, Vol.15 No. 2 pp. 147-161. Journal of Property Valuation & Investment, Vol. 16 No. 3, pp. 297-312. 36. Sirpal, R. (1994). Empirical modeling of the relative impacts of various sizes of shopping centres on the value of surrounding residential properties, Journal of Real Estate Research, vol. 9, no. 4, pp. 487-505. 37. So, H. M., Tse, R. Y. C. & Ganesan, S. (1996). Estimating the influence of transport on house prices: Evidence from Hong Kong, Journal of Property Valuation & Investment, vol. 15, no. 1, pp. 40-47. 38. Tse, R. Y. C. & Love, P. E. D. (2000). Measuring residential property values in Hong Kong, Property Management, vol. 18, no. 5, pp. 366-374. 39. Walden, M. L. (1990). Magnet schools and the differential impact of quality on residential property values, Journal of Real Estate Research, vol. 5, pp. 221-230. 40. Williams, A. (1991). A guide to valuing transport externalities by hedonic means, Transport Review, vol. 11, no. 4, pp. 311-324. Read More