Drivers of rising residential house rent in Wolkite town, Gurage zone, Ethiopia

Figures & data

Table 1. Summary of the review of literatures for determinants of housing rent

Author/s	Title	Methodology	Findings
Belete and Yilma (2020)	Market rent determinants of residential apartments in Addis Ababa Ethiopia	OLS	Number of bedrooms, access to balcony, security of compound, access to lift, access to road and parking area, area of apartment, type of external wall finish, and location or site affect rent of apartment
Dawit and Tsegaye (2018)	Residential pricing in Addis Ababa: Do urban green amenities influence residents’ preferences for house?	Hedonic pricing approach	Type of house construction materials, location, size of compound, renovation and access to secondary school, landscape and nature views, and availability and access to parks influence price of house
Abebaw and Tajani (2021)	Determinants of residential house rent price in Debre Berhan Town	OLS	Access to water, age of house, nearness to amenities, floor material of house, number of bedrooms, access to toilet and land influence the rent of house in the study area
Sebsbe et al. (2017)	Rental house price determinants & affordability in Hawasa city	OLS	Number of rooms, total area occupied by house, access to transport or taxi, hospital or clinic and housing typology affect rental house price.
Egner and Grabietz (2018)	In search of determinants for quoted housing rents: Empirical evidence from major German cities	Macroeconomic-panel model	Mean income, size of academic community, housing unit per capita and population are drivers of quoted housing rental prices
Darfo-Oduro (2020)	Determinants of residential house rental prices in Accra Metropolis	OLS	The main factors affecting house rental prices are structural and neighbourhood characteristics, distance to central business district and market
Amenyah and Fletcher (2013)	Factors determining residential rental prices in Accra, Ghana	Chi-square	Apartment characteristics such as number of bedrooms, availability of amenities (water and electricity supply) and facilities (toilet and bathroom), sharing of apartment facilities, and closeness of apartment to place of work determine rent price.
Luo et al. (2021)	Determinants of housing prices in Dalian City, China: Empirical study based on hedonic price model	Hedonic pricing model	The main factors influencing housing price are the location of apartment or house, the nearby quality of higher education resources, and proximity to public transit service
Wickramaarachchi (2016)	Determinants of rental value for residential properties: A land owner’s perspective for boarding homes	Linear regression model	Floor area, availability of water, electricity, bathroom and neighbourhoods’ characteristics, less congestion or privacy, distance to bus route, tiled floor, and roof with ceiling determine the rental price of housing
Yuan et al. (2017)	Identifying critical factors influencing the rents of public rental housing delivery by PPPs: case of Nanjing	Structural Equation Model	Construction costs, household income, floor area and structure, transportation, and market rent in the same district and public facilities determine public rental housing in China
Degu (2018)	Determinants of residential real estate prices in Bahir Dar city	OLS	House size, estimated price of building, location, broker, construction material, finishing materials, minor building availability and tenure type determine the residential real estate prices
Rezaeian et al. (2019)	The study of determinants of rent housing in Ilam City	Hedonic spatial model	Physical and socio-economic factors influence the rental of residential units more than the neighbourhood and access variables (market, transport, utilities)
Balode and Kamols (2019)	Rental housing market in Riga: Price determinants and lesson keys of Helsinki, Finland	Correlational analysis	Distance from center of business, neighbourhood security, infrastructure, housing quality, employment opportunities, shopping malls, and public transportations have relationship with rent level.
Singla and Bendigiri (2019)	Factors affecting rentals of residential apartments in Pune, India	OLS	The more the floor area and number of rooms, the higher the rental value. As greater the distance form hospitals, postal service, and government office, the lesser the rental value and vice versa. In addition, distance from employment zones, distance from the nearest school/college and the distance from the nearest public transport terminal affect the rental value. Distance from CBD and hospitals implying that higher distances from these places require higher rental value.

Sources: Literature review (2022).

Figure 1. The administrative map of Wolkite town.

Table 2. Measurement and description of variables

Variables	Measurement and description of variable
Rent of house (Pi)	Continuous variable measured in ETB paid to landlord by renter
Education status o(X1)	Dummy variable, 1 if literate and 0 otherwise
Sex of respondents (X2)	Dummy variable, 1 if male and 0 otherwise
Age of respondents (X3)	Continuous variable, measured in year
Marital status (X4)	Dummy variable, 1 if married and 0 otherwise
Household size (X5)	Continuous variable, measured by the number of people in one house
Income (X6)	Continuous variable, measured in ETB
Residency nature (X7)	Dummy variable, 1 if permanent and 0 otherwise (migrant)
Broker involvement in rent (X8)	Dummy variable, 1 if yes and 0 otherwise
Number of bedrooms (X9)	Continuous variable measured in number
Access to bathroom (X10)	Dummy variable, 1 if yes and 0 otherwise
Access to water (X11)	Dummy variable, 1 if yes and 0 otherwise
Access to electricity (X12)	Dummy variable, 1 if yes and 0 otherwise
Flour materials of house (X13)	Dummy variable, 1 if cement or ceramics and 0 otherwise (earthen or wooden)
Location of the house (X14)	Dummy variable, 1 if located at the center of the town and 0 otherwise (peripheral)
Size of the house (X15)	Continuous variable, measured in square meters

Source: Author and literature review (2022).

Figure 2. Conceptual framework of the study.Source: Author development (2022).

Table 3. Frequency and percentage distributions of binary variables

Variables	Category	Frequency	Percentage
Sex of respondent	Male	106	51.96
	Female	98	48.04
Marital status	Married	138	67.65
	Single (other case)	66	32.35
Education level	Illiterate (no read and write)	70	34.31
	Literate	134	65.69
Residence condition	Permanent (resident)	68	33.33
	Temporal (migrants)	136	66.67
Broker involvement	Yes	176	86.27
	No	28	13.73
Access to electricity	Yes	195	95.59
	No	9	4.41
Access to bathroom	Yes	75	36.76
	No	129	63.24
Access to water	Yes	197	96.57
	No	7	3.43
Floor materials of the house	Ceramics or cement	68	33.33
	Wood or earthen	136	66.67
Inflation (increase in rent)	Yes	98	48.04
	No	106	51.96
House location	Center of town	76	37.25
	Peripheral of the town	128	62.75

Source: Survey Result (2022).

Table 4. Descriptive statistics of discrete and continuous variables

Variable	Obs.	Mean	Std. Dev.	Min.	Max.
Age of household	204	31.99	7.12	19	50
Household size	204	2.04	.87	1	5
Monthly income	204	7758.10	2431.50	2500	20000
Monthly rent	204	2162.75	919.00	700	7500
Number of bedrooms	204	1.16	.81	0	3
House size (area)	204	43.36	11.60	25	70

Source: Survey Result (2022).

Table 5. Multicollinearity test

Variable	VIF	1/VIF
Number of bedrooms	3.03	0.32996
The house-built materials or floor materials	3	0.33353
House size	2.64	0.37949
Household size	2.4	0.41668
Location of the house	2.31	0.43242
Natural logarithm of monthly income of households	1.62	0.61768
Education level of households	1.54	0.64922
Involvement of house broker while renting home	1.32	0.75918
Access to electricity at house	1.23	0.81219
Access to water at house/compound	1.18	0.84906
Access to bathroom at the house/compound	1.06	0.93943
Increase in rent due to the current inflation	1.17	0.85705
Sex of households	1.09	0.92036
Residency nature of households	1.05	0.9523
Mean VIF	1.76

Source: Survey Result (2022).

Table 6. Heteroscedasticity test result

Breusch–Pagan/Cook–Weisberg test for heteroskedasticity

Ho: Constant variance and H1: No constant variance

chi^2(1) = 34.19; Prob > chi^2 = 0.0650

Source: Survey Result (2022).

Table 7. The estimated results of the OLS model

Ln monthly rent (dependent variable)	Coef.	Std. Err.	t	P > t	[95% conf. interval]
Ln monthly income	0.243	0.060	4.02	0.000	[0.124, 0.362]
House location	0.296*	0.053	5.620	0.000	[0.192, 0.400]
Inflation	0.013	0.038	0.350	0.728	[−0.061, 0.088]
House size (area) in meter square	0.004	0.002	1.550	0.123	[−0.001, 0.009]
House-built material/floor material	0.195**	0.062	3.140	0.002	[0.072, 0.317]
Access to bathroom	−0.022	0.035	−0.620	0.535	[−0.092, 0.048]
Access to water	0.108	0.101	1.070	0.287	[−0.091, 0.307]
Access to electricity	0.131	0.090	1.460	0.147	[−0.046, 0.309]
Number of bedrooms	0.186*	0.038	4.920	0.000	[0.112, 0.261]
Involvement of house broker	0.155*	0.056	2.780	0.006	[0.045, 0.264]
Residency status	−0.010	0.041	−0.250	0.801	[−0.091, 0.071]
Education level	0.076***	0.045	1.700	0.092	[−0.012, 0.164]
Household size	0.074**	0.030	2.420	0.016	[0.014, 0.134]
Sex of respondent	0.064***	0.035	1.860	0.065	[−0.004, 0.132]
Age of respondent	−0.001	0.002	−0.320	0.747	[−0.006, 0.004]
_cons	4.324*	0.557	7.760	0.000	[3.225, 5.423]

*, ** and *** represent significance level at 1%, 5% and 10%, respectively. Number of Obs. = 204, F(15,188) = 29.6, Prob. > F = 0.0000, R-Squared = 0.7031, Adj. R-squared = 0.6794.

Source: Survey Result (2022).

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