Impact of the Federal University of Technology, Akure on Residential Property Values in Akure, Nigeria. Victoria Amietsenwu BELLO and Oluwasola ADEBISI, Nigeria Key words: Residential Property Values, Population, University. SUMMARY The presence of a university in a derelict area may initiate the transformation of the entire neighbourhood through the action of development, regenerating houses that may attract students, staff, and those providing support services. The paper therefore posits to examine the impact of the Federal University of Technology Akure on residential property values around the university. Structured questionnaire were administered on tenants of 251 residential houses of which 176 were retrieved for analysis. Also, records of students population and those living on campus were gotten from the students affairs division of the institution. The data collected were analysed using descriptive statistics and simple regression analysis. The result reveals that greater number of the student population lives off campus and this impacted greatly on rental value of residential properties occupied by them. The study therefore, recommends that government should build more hostel accommodation in the universities and renovate old ones. If this is done, many of the student population will be accommodated and this will reduce the pressure of student in the housing market thereby reducing rental value of residential properties. 1/16
Impact of the Federal University of Technology, Akure on Residential Property Values in Akure, Nigeria. Victoria Amietsenwu BELLO and Oluwasola ADEBISI, Nigeria 1. Introduction University is viewed as a magnet in the metropolitan area that attracts people and economic activity towards it (O Flaherty, 2005). In recent time the Nigeria government have shown strong commitment to growth in higher education sector by the establishment of more institutions especially universities and the liberalization of its ownership. While this is the focus of the government little or no attention is given to the provision of accommodation in the university to meet the rising student population. As a result of this majority of the student population take solace in the rental market for alternative accommodation. The demand has resulted in the establishment of niche market because student s market especially appears to be robust. Hence, Peacocke (1999) opined that the major characteristics of students demand is that it monopolizes the market thereby reducing supply to other tenant groups. For instance, close to 80% of students populations of the Federal University of Technology, Akure are living in privately rented accommodation. The result of this is a sharp increase in rent since the available housing accommodation even in the rental market are not enough to meet the demand. In the past, houses were completed by landlords or property investors, but today, students struggle to make payment even when such accommodation are yet to be completed. The landlords and investors exploit this situation and rents are arbitrary fixed and outrageous. Therefore, this study aims at investigating the impact of the location of the Federal University of Technology, Akure on the rental values of neighbouring residential properties over the years. 2. University Location and Neighbouring Residential Property Rental Values University as a social institution is seen as a great system concerned with imparting knowledge and skills, which help an individual to participate in the society 2/16
(Ramon-Yusuf, 2003). Babalola and Okediran (1997) views university education as that which facilitates creation of knowledge and innovation for the overall socio-economic empowerment of the individual and community development. Historically in Nigeria, majority of the universities were located at the community outskirts so as to create conducive environment for learning; less difficulty in getting required land size and the minimum financial implication attached. However, these universities including the Federal University of Technology, Akure have enjoyed circumferential expansion in their neighborhoods primarily because of the functions they perform. A university may generate different sort of impacts in a neighborhood. The presence of a university in a derelict area may initiate the transformation of the entire neighbourhood through the action of development, regenerating houses in order to attract students, staff, and those providing support services to live nearby (Perry and Wiewel, 2005). More also, universities can also function completely apart from the surrounding neighborhoods, ignoring social problems (Anamaria and Melchior, 2007). Anamaria and Melchior (2007) used two concepts to analyze the impacts produced by the presence of a university in an area: to include magnets and enclave. The first concept used to describe the impacts of large land owners such as the university is the magnet. In urban terms, magnet represents a concept or metaphor which describes a territory in the metropolitan area which attracts people and economic activity towards it. Universities can be qualified as magnets since they have the power of attracting many students, faculties and staff as well as business and institutional activities to a specified region in a town. Universities, when acting as a magnet, besides attracting uses that promote urban and economic growth, attracts activities regarded as damaging by particular groups in the society (Anamaria and Melchior, 2007). The second concept used to describe the impact of large land owners such as the universities is the enclave. A new enclave represents a self contained place or region of the 3/16
city, with uses or morphology different from the one of the surrounding neighborhood (Anamaria and Melchior, 2007). According to Perry and Wiewel (2005), a university has traditionally seen itself as an enclave, removed enough from the immediacy and demands of modern life to produce the knowledge and information with which to better understand the society. Depending on the externality, the role of universities as magnets most times overcomes their features as enclaves. Therefore, the concepts of magnets and enclaves represent an important methodological tool in the analysis of the impacts and externalities by the universities on their neighborhoods (Anamaria and Melchior, 2007). Universities and colleges primarily acquire land and structures that support their core mission or immediate growth demands, it is however not uncommon for surrounding communities to criticize universities for their unresponsive development policies or lack of a plan to mitigate negative spillover effects (Sungu-Erylimaz, 2009). For neighbourhood residents, some of the major concerns relate to quality of life issues, such as conversion of houses and other buildings to student occupancy; upward pressure on rents; adaptation of shops and facilities to student markets; and increase in traffic, noise, and parking problems (Harasta, 2008) Universities according to Vandergrift et al (2009) are said to provide culture, high technology, recreational facilities, open space, sporting facilities, and fun. A university campus is often the focal point of a municipality. Green areas, water bodies, and open space are all common on campuses. Thus, through higher demand for this unique confluence of amenities, a campus itself may cause house prices to be 10% higher in New Jerseys (Vandegrift et al 2009). Anamaria and Melchior (2007) in the study carried out on the impact produced by the presence of university campuses on land and property values, found out that in the immediate area of influence (a radius of 1km from the campus) of the two campuses studied, land and property values were expected to be impacted by proximity of the universities. Also, Haurin and Brasington (1996) employed the hedonic price model that 4/16
includes an accessibility index, arts index, population growth index, and recreation index among several other house characteristic variables. Distance to a central business district was positive, but not statistically significant. The four indices used were all statistically significant except the population growth rate index. This suggests that housing prices within towns that provide arts and recreation increase due to the amenity value present. Since college towns are well known for providing such value to those who are not enrolled, it is hypothesized that housing prices in university towns should be higher due to the availability of recreation and arts. A study carried out by Janmaat (2007) on the factors affecting residential property rental values in a small historic Canadian university town revealed that view and average sound level were not statistically related to home price. Peak sound level is priced with one decibel increase reducing the average house price by 2%. The study also revealed that given the high population of student tenants in Wolfville, tenants are unlikely to live in areas zoned single family residential, these results suggest that rental externalities - either due to student tenants or landlord practices are having a strong negative impact on rental values. Ankali (2007) carried out a study on the impact of tertiary institutions on residential property values in the Federal Polytechnic, Ede and found out that the establishment of the polytechnic led to the influx of people into the town, thereby, making the residential property market subsector experience a moderate increase in rental values between 1994 and 1997. Another slight increase was also experienced between 1998 and 1999 and the highest rental increment was observed between 2001 and 2005. However, tenement properties close to the Polytechnic experienced the highest rental growth rate. Owusu-Edusei, Espey and Lin (2007) established that there is a positive value associated with proximate location to schools and a negative value associated with greater than average distance from schools. The study found out that location within 800 feet, about 5/16
0.15 miles of elementary schools produces housing values from about 8% to 13% higher than houses located 800 feet to about 200 miles away, while houses located more than 2 miles from an elementary school have values about 10% less. Location within 800 feet of a middle school could increase housing values as much as 12% relative to houses 800 feet to about 2.2 miles away, while location further than 2.2 miles negatively impacts housing prices by about 18%. The study further found out that High schools are exception to this, with very close proximity generating a negative impact on surrounding residential property rental values as high schools tend to have more night time activities such as night parties, traffic e.t.c. Shigeru, Hiroshi and Qiang (1999) carried out a study on the effects of University of Kansas on residential house values by using non-parametric assessment. Geographical information system overlay and near was used to determine the distance of each household to the institution. 6,415 residential sales were analysed. The study found out that house adjacent to the university values more than 40% higher than the comparable house located 2000meters or more away from it. It was also found out that as the distance to the university gets large, such effects initially declines rapidly and then in a more moderate pace. The study further found out that the reason for the university effect to disappear around 1800 meters is that the walking distance of 20 minutes is the maximum an average person will choose to commute on foot. Ajayi and Nwosu (2010) examined the relationship between the Federal University of Technology, Akure and property values. Using the Pearson s product moment correlation, the study revealed a strong positive relationship between population of students and residential property values. 3. Research Methodology The data for the study was collected from the student affairs division of the Federal University of Technology, Akure and the tenants of the residential properties around the South gate of the institution. The sample frame of the houses using physical counting is 726. Cochran (1963) formula as modified by Isreal (2002) was used to arrive at the sample size 251 for the study. Questionnaires were administered to the tenants of the sampled residential properties in the study area to elicit information on rents and type of 6/16
accommodation occupied while data on students population and those living on campus were gotten from the students affairs division. Out of the two hundred and fifty one (251) questionnaires distributed to the tenants of the residential properties in the study areas, one hundred and seventy six (176) were dully filled and returned for analysis. Descriptive statistics in form of frequency tables were used to analyse the characteristics of the respondents while inferential statics in form of correlation and linear regression analysis were used to analyse the relationship between the population of students living in the private rented sector/distance from FUTA and property values. The form of regression function used is RV = a 0 + b DIST + e ---------------------------------- (4) RV = Rental Value ( ) a = Y intercept DIST = Distance from FUTA in (M) b = Coefficient of DIST Table 1: Operationalization of Variables Variable Definition of Variables Measurement RV Rental Value Naira ( ) DIST Distance Meters(M) 4. Data Analysis And Discussion Of Results (a) Background Information on the Tenants of the Neighbourhood of the Federal University of Technology, Akure. 7/16
Table 2: Distribution of Tenants According to Educational Qualification and Monthly Income Educational qualification Monthly Income 10000-21000- 61000-101000 and 10000-20000 20000 60000 100000 above No formal education 0(0.0) 0(0.0) 0(0.0) 0(0.0) 0(0.0) Primary school leaving 0(0.0) 0(0.0) 0(0.0) 0(0.0) 0(0.0) certificate Secondary school leaving 87(49.4) 9(5.1) 0(0.0) 0(0.0) 87(49.4) certificate O.N.D 8(4.5) 8(4.5) 0(0.0 0(0.0) 8(4.5) H.N.D 0(0.0) 12(6.8) 0(0.0) 0(0.0) 0(0.0) B.Sc 13(7.4) 4(2.3) 8(4.5) 12(6.8) 13(7.4) M.Sc 0(0.0) 0(0.0) 0(0.0) 16(9.1) 0(0.0) Total 108(61.3) 33(18.7) 8(4.5) 28(15.9) 108(61.3) The result from Table 2 indicates that 61.3% of the respondents are low income earners who earn between 10,000:00 and 20,000:00. This can be attributed to the fact that majority of the residents around the institution are students who depend on their parents. However, the Table reveals that the respondents have at least secondary school education, which made questionnaire administration less difficult. Table 3: Distribution of Tenants According to Type of Residential Property Residential Property Type % of Tenants in each Residential property Type A room 81 (46.0) A room self contain 31 (17.6) 1 bedroom flat 4 (2.3) 2 bedroom flat 16 (19.1) 3 bedroom flat 44 (25.0) Total 176(100.0) Table 3 shows that 46.0% of the respondents residing within the neighbourhood of the Federal University of Technology, Akure (FUTA), live in tenement apartments while 17.6%, 2.3%, 19.2% and 25% of them live in a room self contain, one-bedroom, two and three bedroom flat respectfully. However, some of the tenants around FUTA share rooms in flats and other residential property types. 8/16
(b) The Relationship between Students Population and Residential Property Values Table 4: Population of Undergraduate Students of FUTA (PSF), Population of Students Living on Campus (PSFC) and Population of Students of FUTA living in the Private Rented Sector (PSFPRS). Academic Year PSF PSFC PSFPRS PSFPRS in % 2006/07 6264 1515 4749 75.81 2007/08 6795 1515 5280 77.70 2008/09 8054 1515 6539 81.18 2009/10 9831 1515 8316 84.58 2010/11 12556 1923 10633 84.68 Source: Students Affairs Division, 2011 Table 5: Population of Students of FUTA Living in the Private Rented Sector (PSFPRS) and the Average Rental Values of Residential Properties within the Neighbourhood of the Institution between 2006/07 and 2010/11 Year PSFPRS Average Rental values per annum of accommodation types ( ) Tenement A room self contained Three bedroom flat 2006/07 4749 20130.00 42000.00 89000.00 2007/08 5280 23200.00 47300.00 97500.00 2008/09 6539 26000.00 53500.00 116000.00 2009/10 8316 31467.74 60263.15 141250.00 2010/11 10633 3540.00 65800.00 152083.33 Source: Students Affairs Division, 2011 Table 6: Correlation Coefficient of the Rental Values of the three types of Residential Properties and Populations Population Tenement A room self contain 3 bedroom flat Population 1 Tenement 0.988 1 A room self contain 0.975 0.995 1 3 bedroom flat 0.975 0.992 0.992 1 Table 4 shows the population of undergraduate students of FUTA, the population of students living in the campus hostels and the population of students living in the private 9/16
rented sector. The result from the Table shows that there was a steady growth in the number of students admitted between 2006/07 and 2010/11 academic session. However, number of bed spaces remained 1515 between 2006/07 and 2009/2010. The increase in bed space to 1923 in 2010/2011 academic session was as a result of additional 408 bed spaces provided by the university management. This implies that the student growth has generally overtaken the accommodation expansion of the institution. Table 5 shows the average population growth of students of the Federal University of Technology, Akure living in the private rented sector and the average rental values of a room, a room self contained and three bedroom flat between 2006/07 and 2010/11 in the study area. This implies that reliance on the private rented sector by the students has grown over time due to the low levels of accommodation provision by the institution. This in turn has resulted to sharp increments in rents and evolvement of niche market for the students (supplies being adapted to meeting the needs of a specialized group and is reluctant to meeting demand from another source). This is in conformity with Peacocke, (1999) which states that one of the complaints relating to student demand is that it monopolizes the rental sector. Table 6 shows the correlation of rental values of tenement apartments, self contained and three bedroom apartments with the students population living in the private rented sector. The Table shows that there is high positive correlation between population growth and rental values. The correlation coefficients between population and tenement apartments, self contained and three bedroom apartments are 0.988, 0.975 and 0.975 respectively. 10/16
(c) Impact of distance from the Federal University of Technology on Residential Property Values. Table 7:Average Residential Property Rental Value and Distance from FUTA South Gate DISTANCE (M) TENEMENT A ROOM SELF 3 BEDROOM FLAT CONTAIN 100-200 44,200 80,000 176,000 201 300 44,000 80,100 175,000 301 400 40,000 70,000 175,000 401 500 40,000 70,000 165,100 501 600 37,000 68,000 170,000 601-700 35,000 65,000 169,000 701 800 31,400 60,200 168,300 801 900 26,500 55,000 180,000 901 1000 24,800 50,000 175,000 Table 7 shows the average rental values of different accommodation types at various distances from FUTA. Table 8: Summary of Model a room Model R R² Adjusted R² Std. Error of the Estimate 1 0.631 a 0.398 0.358 4985.56616 Table 9: Analysis of Variance of a room Model Sum of DF Mean F Sig. Squares Square Regression 2.464E8 1 2.464E8 9.913 0.007 a Residual 3.728E8 15 2.486E7 Total 6.192E8 16 Table 10: Regression Coefficients of a room Model Unstandardized Coefficients Standardized Coefficients T Sig B Std. Error Beta Constant 39088.379 2288.598 17.080 0.000 Distance -6.806 2.162-0.631-3.148 0.007 11/16
Table 11: Summary of Model a room self contain Model R R² Adjusted R² Std. Error of the Estimate 1 0.721 a 0.520 0.486 7276.05317 Table 12: Analysis of Variance of a room self contained apartment Model Sum of DF Mean F Sig. Squares Square Regression 8.026E8 1 8.026E8 15.160 0.002 a Residual 7.412E8 14 5.294E7 Total 1.544E9 15 Table 13: Regression Coefficients of a room self contained apartment Model Unstandardized Coefficients Standardized Coefficients T Sig B Std. Error Beta Constant 82075.924 3734.923 21.975 0.000 Distance -12.813 3.291 -.721-3.894 0.002 Table 14: Summary of Model of a 3 bedroom flat Model R R² Adjusted R² Std. Error of the Estimate 1 0.147 a 0.022-0.054 29750.96821 Table 15: Analysis of Variance of a 3 bedroom flat Model Sum of DF Mean F Sig. Squares Square Regression 2.534E8 1 2.534E8 0.286 0.602 a Residual 1.151E10 13 8.851E8 Total 1.176E10 14 Table 16: Regression Coefficients of a 3 bedroom flat Model Unstandardized Coefficients Standardized Coefficients T Sig B Std. Error Beta Constant 162150.295 13824.386 11.729 0.000 Distance -6.963 13.012-0.147-0.535 0.602 12/16
From the Model summary in Table 8, the adjusted R² shows that 35.8% of the variation in rental values of a room apartment is attributed to the independent variable (distance). This is evident by its level of significance which is 0.007. This implies that the distance from the Federal University of Technology, Akure significantly affects the rental value of single room apartments. In Table 10, the zero order correlation indicates that there is a strong negative relationship between distance from the Federal University of Technology, Akure and the rental value of a room residential apartment. This is significant at 0.007. This implies that rental value for a room residential apartment decreases as distance from The Federal University of Technology increases and vice versa. Thus, the regression model can be written as: RV 1 rm = 39088.379 6.806 DIST Where RV 1 rm = Rental value for I room apartment ( ) DIST = Distance from FUTA in (M) From the Model summary in Table 11, the adjusted R² shows that 48.6% of the variation in rental values of a room self contained apartment is attributed to the independent variable. This is evident by its level of significance which is 0.002. This implies that distance from the Federal University of Technology, Akure significantly affects the rental value of a room self contained apartment. In Table 13, the zero order correlation indicates that there is a strong negative relationship between distance from the Federal University of Technology, Akure and the rental value of room self contained apartment. This is significant at 0.002 significant levels. This implies that rental value of a room self contain apartment decreases as distance from The Federal University of Technology increases and vice versa. Thus, the regression model can be written as: RV 1 brm = 82075.924 12.813 DIST Where RV 1 brm = Rental value for a room self contained apartment ( ) DIST = Distance from FUTA in (M) From the Model summary in Table 14, the adjusted R² shows that -5.4% of the variation in rental values of three bedroom apartments is attributed to the independent variable 13/16
(distance). This is evident by its level of non-significance at 0.607. This implies that distance from the Federal University of Technology, Akure has no significant impact on the rental value of three bedroom apartments. In table 16, the zero order correlation indicates that there is a negative relationship between distance from the Federal University of Technology, Akure and three bedroom apartment rental values but not significant at 0.602. Thus, the regression model can be written as: RV 3 brm = 162150.295 6.963 DIST Where RV 3 brm = Rental value for I room apartment ( ) DIST = Distance from FUTA in (M) These results from the regression tables imply that the distance from the Federal University of Technology, Akure significantly impact the rental values of residential properties negatively apart from three bedroom flats which is not significant at 0.607, though negatively correlated. 5. Conclusion and Recommendation The paper show the trend of rental values of selected accommodation types against the population growth of students living in the private rented sector and the positive correlation between these two variables between the year 2006/07 and 2010/11. The paper also reveals that an average of 80.79% of the population of students of FUTA lived in the private rented sector between year 2006/07 and 2010/11. Furthermore, it has given an elaborate account on the relationship that exists between distance from the Federal University of Technology, Akure and rental values of residential properties selected. An increase in the distance of a room/self contained apartment from the Federal University of Technology, Akure will result to a decrease in the rent and vice versa. It is therefore, recommended that the government and higher education institutions should be encouraged to make a clear and definite statement concerning provision of oncampus accommodation. This will reduce the pressure on the private rented sector; mitigate high rents and exploitation of students. Also analysis of the likely impacts of higher educational institutions on local rental market should be integral to the establishment and 14/16
expansion plans of every higher educational institution. 6. Reference Anamaria de Aragao C.M. and Melchior S.N. (2007). The Impact of University Campuses on Disperse Urban Contexts: Case Study of Brasillia, Brazil. Lincoln Institute of Land Policy. Ajayi, M. A. and Nwosu, A. E. (2011). Impact of Tertiary Institutions of Learning on Proximate Residential Property Values: Case Study of the Federal University of Technology, Akure, Nigeria. International Journal of Research in Education, 3 (6) 128 136 Ankeli, A. I. (2007). An Empirical Study on the Impact of Tertiary Institutions on Residential Property Rental Values in a Developing Nation (A Case of Federal Polytechnic, Ede, Osun State). International Journal of Sciences, Engineering and Environmental Technology, 2 (1), 194 199. Babalola, J.B. and Okediran, A. (1997): Functions of Management and the Elastic Organization. Van Nostrad Reinhold, New York. Cochran, W. G. (1963). Sampling Techniques, 2 nd Ed, New York. John Wiley and Sons, inc. Harasta, J. (2008).Town Gown Relations: University and Neighbourhood Leaders Perceptions of College and Community Relations. Ph.D. Thesis. Wilmington University, New Castle, DE, July Haurin, D., and Brasington, D. (1996). School Quality and Real House Prices: Inter-and Intra metropolitan Effects. Journal of Housing Economics 5, 351-68. Israel, G. D. (2002). Sampling The Evidence Of Extension Program Impact. Program Evaluation and Organizational Development, IFAS, University of Florida. O Flaherty, B. (2005). City economics. Cambridge: Harvard University Press. Owusu Edusei, K., Epsey, M., and Lin, M. (2007). Schools and Residential Property Values. Journal of Agriculture and Applied Economics, 39, 211 221. Peacock, H. (1999). Fallow Field, Roof, September/October. Perry, D. C. and Wiewel, W. (2005). The University as Urban Developer. Armonk, NY: M.E. Sharpe and the Lincoln Institute of Land Policy. Ramon-Yusuf, (2003). The Role of National University Commission in Nigeria Universities. Abuja: NUC Shigeru, I., Hiroshi, M and Oiang, W. (1999).Nonparametric Assessment of the Effects of Neigbouring Land Uses on Residential House Values, American Real Estate and Urban Economics Association annual meeting in New York. 15/16
Vandegrift,D. Lockshiss, A. and Lahr, M. (2009). Town versus Gown: The Effect of a College on Housing Prices and the Tax Base. The College of New Jersey, Rutgers, The State University of New Jersey. BIOGRAPHICAL NOTES Dr (Mrs.) V. A. BELLO is a Senior Lecturer in the Department of Estate Management, Federal University of Technology Akure, Ondo State, Nigeria. She is the Post Graduate Cordinator in the Department of Estate Management. Dr (Mrs.) Bello graduated from the Department of Estate Management, Obafemi Awolowo University, Ile Ife with a Bachelor of Science Degree (Second Class Upper Division). She obtained a Master of Technology and PhD in Estate Management from the Federal University of Technology, Akure, Nigeria. Her Research interest is in Property Valuation and she has published in many international and local journals. She is a Registered member of the Nigerian Institution of Estate Surveyors and Valuers in Nigeria. CONTACTS Dr (Mrs.) Bello, Victoria Amietsenwu Federal University of Technology, Akure Department of Estate Management P.M.B. 704, AKURE Ondo State Nigeria Tel: 234 803-668 - 8879 Email: vicbellofuta@yahoo.com 16/16