1# RECORD 75d0711aebd70fde400d1a6a49265e443bbe18842# TITLE: The value of environmental (dis)amenities in the urban housing market: Evidence from Khulna, Bangladesh3# YEAR: 2020.0 VENUE: nan DOI: 10.1016/j.jum.2020.02.0014# AUTHORS: Md. Sariful Islam; Ruqaiya Hossain; M. Morshed; S. Afrin5# PAGES: 12 CANDIDATE TABLE PAGES (1-based): [9]6# ABSTRACT: Abstract Pressure of rapid urbanization on abiotic environmental parameters in the residential blocks is one of the major challenges of city corporation authorities in Bangladesh. Environmental degradation often reduces property value in the housing market. However, the studies attempted to assess the value of environmental amenities and dis-amenities in the housing market of Bangladesh are scarce. Hence, this study examines the economic value of environmental (dis)amenities in the housing market of Khulna city, Bangladesh. Hedonic pricing method was used to assess the value of four environmental attributes - ventilation, open space, waterlogging and landfill by waste in the housing market. The study found these environmental attributes explain the variation of house rents significantly. Environmental amenities–a hundred-meter reduction in proximity to open space leads to a 2 percent increase in rent while a one discrete unit increase in a 5-point ventilation improvement scale from very poor to excellent results in 5.4 rent increase–are found as rent booster in the urban housing market. In contrast, waterlogging and landfill by waste reduce house rents by 3.7 and 9.8 percent for one unit increase in the respective 5-point degradation scales. The further analysis showed that the rental price of planned residential areas is more sensitive to ventilation and landfilling while that of unplanned areas is more sensitive to both environmental dis-amenities – water-logging and landfilling. Furthermore, our study computed the implicit price of these environmental (dis)amenities in the housing markets of Khulna city. This study suggests improving environmental quality boosts house rent, which in turn attracts environment-friendly urbanization.789===== PAGE 1 (score 2, numeric-paren 0, table-words 0, var-words 2) =====1011Islam, Md. Sariful; Hossain, Ruqaiya; Morshed, Md. Manjur; Afrin, Sonia12Article13The value of environmental (dis)amenities in the urban14housing market: Evidence from Khulna, Bangladesh15Journal of Urban Management16Provided in Cooperation with:17Chinese Association of Urban Management (CAUM), Taipei18Suggested Citation: Islam, Md. Sariful; Hossain, Ruqaiya; Morshed, Md. Manjur; Afrin, Sonia (2020) :19The value of environmental (dis)amenities in the urban housing market: Evidence from Khulna,20Bangladesh, Journal of Urban Management, ISSN 2226-5856, Elsevier, Amsterdam, Vol. 9, Iss. 2, pp.21180-190,22https://doi.org/10.1016/j.jum.2020.02.00123This Version is available at:24https://hdl.handle.net/10419/27138525Standard-Nutzungsbedingungen:26Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen27Zwecken und zum Privatgebrauch gespeichert und kopiert werden.28Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle29Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich30machen, vertreiben oder anderweitig nutzen.31Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen32(insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten,33gelten abweichend von diesen Nutzungsbedingungen die in der dort34genannten Lizenz gewährten Nutzungsrechte.35Terms of use:36Documents in EconStor may be saved and copied for your personal37and scholarly purposes.38You are not to copy documents for public or commercial purposes, to39exhibit the documents publicly, to make them publicly available on the40internet, or to distribute or otherwise use the documents in public.41If the documents have been made available under an Open Content42Licence (especially Creative Commons Licences), you may exercise43further usage rights as specified in the indicated licence.44 https://creativecommons.org/licenses/by-nc-nd/4.0/454647===== PAGE 9 (score 64, numeric-paren 31, table-words 9, var-words 15) =====4849ventilation amenity score is 2.73 in Khulna city. This ventilation score is found to vary from 2.5 to 2.99 and this score in planned50areas is just 0.4 point higher than unplanned areas. Therefore, ventilation service is relatively poor in unplanned areas compared to51that service in the planned areas as the higher score reflects the better ventilation service. On the other hand, the lower score in the52waterlogging and landfilling scale reflects less severe waterlogging and landfilling situations. Households in planned areas perceived53less severity of waterlogging (2.58) than that of waterlogging (3.17) in unplanned areas. Similarly, overall landfilling severity score is543.33, and households in planned areas experienced landfilling severity 2.90 point which is 0.67 point less than households in55unplanned areas of Khulna city. Finally, average distance of open spaces from houses is about 376-m in the city and this distance is56around 90-m less for residents in planned areas than unplanned areas. Therefore, residents of planned areas enjoy better ventilation,57have close proximity to open spaces, experience less severe waterlogging and less landfill degradation than the residents of unplanned58areas. This is because newly developed planned areas are far from the city center and these areas have more open spaces nearby these59residential blocks.603.2. Determinants of house rents in Khulna city61The results estimated from the hedonic pricing approach of equation (7) are reported in Table 3. It depicts that income of62households, the number of rooms, space for garages, environmental (dis)amenities and distances from schools and hospitals sig-63nificantly determine house rent in Khulna city though some degree of effects on rent differs between planned and unplanned areas.64Column 1 of Table 3 reports the impact of aforementioned attributes on house rent of Khulna city. We found income is positively and65significantly associated with house rent at 1 percent level of significance if all other things being constant, as expected, though the66level of impact is low. A 0.3 percent increase in house rent is associated with a rise in household income by BDT 1000 per month. In67addition, both of the structural characteristics – the numbers of rooms and space for the garage – positively and significantly increase68house rent at 1 percent. House rents are 39, 48 and 61 percent higher for two, three and four-room flat respectively than that of one-69room flat. In addition, the availability of garage space increases house rent by 15 percent at 1 percent level of significance. However,70age of the house though shows expected negative association, it does not have significant impact on house rent. Among the71neighborhood attributes, only proximity of stationery shops does not have a statistically significant impact on house rent in the city as72a whole. However, an increase in distances by 1 km to hospitals and schools from houses significantly reduce rent by 17 and 2473percent respectively.74However, house rent in terms of structural and neighborhood characteristics between planned and unplanned areas differ. Income75significantly and positively explains house rents of unplanned areas while it does not have significant impact on house rent in the76planned areas of Khulna city. Similarly, the number of rooms is also found as significant determinant in unplanned areas while it is77not planned areas. In contrast, space for the garage significantly increases house rent by 18 and 12 percent in both planned and78unplanned areas. Distances of hospitals from houses significantly reduce the rent in both planned and unplanned areas though79Table 380Determinants of house rent: a hedonic pricing approach.81Dependent variable82(1)83(2)84(3)85Log of rent86Overall87Planned areas88Unplanned areas89Explanatory variables90Coefficient91SE92Coefficient93SE94Coefficient95SE96Income (‘000’ BDT/month)970.0031***980.0009990.00151000.00161010.0037***1020.0013103Structural characteristics104Age of house (Years)105−0.00231060.0018107−0.00851080.0064109−0.00191100.0018111One rooms (=1)112Base category113Two rooms (=1)1140.3915***1150.09241160.4072***1170.0835118Three rooms (=1)1190.4772***1200.0908121−0.01371220.11511230.4943***1240.0814125Four rooms (=1)1260.6137***1270.10301280.07771290.12911300.6826***1310.1178132Garage (=1)1330.1549***1340.04251350.1830**1360.08031370.1240**1380.0569139Environmental characteristics140Ventilation (1 = Poor to 5 = Excellent)1410.0540***1420.01481430.0785**1440.03241450.0440**1460.0174147Open space (meters)148−0.0002***1490.0001150−0.00021510.0002152−0.0003***1530.0001154Waterlogging (1 = No to 5 = Extreme)155−0.0369**1560.0146157−0.0601*1580.0309159−0.02011600.0164161Landfill (1 = No to 5 = Extreme)162−0.0980***1630.0163164−0.1218***1650.0381166−0.0941***1670.0183168Neighborhood characteristics169Stationary shop distance less than 0.5 km170Base category171Stationary shop distance within 0.5–1 km1720.02761730.05121740.03091750.0925176−0.00061770.0646178Stationary shop distance above 1 km179−0.05631800.08951810.03291820.2432183−0.10151840.0983185Hospital distance (km)186−0.1665***1870.0505188−0.1722**1890.0756190−0.1860**1910.0882192School distance (km)193−0.2411***1940.0438195−0.04761960.1283197−0.2540***1980.0745199Planned areas (=1)2000.03432010.0448202Constant2039.3288***2040.24772059.9765***2060.41042079.3508***2080.3778209Observations21020121172212129213R-squared2140.79042150.60722160.7954217Significant at: ***p < 0.01, **p < 0.05, *p < 0.1.218Md. S. Islam, et al.219Journal of Urban Management 9 (2020) 180–190220187221222223===== PAGE 10 (score 12, numeric-paren 0, table-words 3, var-words 6) =====224225distances of schools significantly explain house rent of unplanned areas only. The proximity of stationery shops does not explain226house rent of any areas.227After controlling all of the aforementioned determinants of house rents, all four environmental attributes – ventilation, open228space, waterlogging and landfill – have a statistically significant impact on house rent in Khulna city. Segregated results showed in229column 2 and 3 of Table 3 report that only open space proximity is not found significantly associated with rent in planned areas while230only waterlogging does not have a significant impact on rent in unplanned areas and all other attributes exhibit expected and231significant impacts in both planned and unplanned areas. In accordance with our expectation, environmental amenities increase232while dis-amenities reduce house rent significantly. In Khulna city, households are willing to pay (WTP) 5.4 percent more as rent for a233one-unit discrete increase in 5-point ventilation amenity scale. Such WTPs for a one-unit improvement in ventilation of planned and234unplanned areas are 7.9 and 4.4 percent respectively. It implies that residents of planned areas are willing to pay almost 1.8 times as235much as those of unplanned areas are willing to pay for discrete improvement in ventilation. Similarly, house rent increases 2236percent1 for every 100-m reduction in proximity of houses to open spaces while such increase is 2 and 3 percent for planned and237unplanned areas, respectively. In contrast, environmental (dis)amenities – waterlogging and landfill – around the houses reduce rent238by 3.7 and 9.8 percent in Khulna city respectively for one-unit increase in 5-point degradation scale. These findings are statistically239significant at 5 and 1 percent level, respectively. In both planned and unplanned areas, households are more sensitive to landfilling240induced degradation than waterlogging induced degradation. In planned areas, the landfilling induced rent reduction is 12 percent,241which is almost double of waterlogging induced rent reduction and in unplanned areas, the landfilling induced rent reduction is242about 4.5 times the water-logging induced rent reduction.2433.3. Value of environmental (dis)amenities in housing market244In this section, we estimate the value of environmental (dis)amenities from estimated coefficients of equation (7) and a set of245threshold values for environmental parameters reported in Table 4. In 5-point ventilation scale, point 4 and 5 represent good and246excellent ventilation while point 4 and 5 in both waterlogging and landfilling scale represent bad to extreme degradation. Distance to247open space below 500-m is considered as close proximity to this amenity. For this reason, we use these values as the threshold248parameters for these environmental attributes. Based on these threshold values of the parameters, we quantify the value of en-249vironmental (dis)amenities in the housing market. The results show that for environmental amenities - ventilation and open space,250households on an average are willing to pay BDT 1977 (USD 24) and BDT 984 (USD 12) per month more than average rent,251respectively. On the other hand, for environmental (dis)amenities - waterlogging and landfill by waste, these households are willing252to pay BDT 1988 (USD 24) and BDT 3496 (USD 42) less than average house rent in Khulna city. Therefore, ventilation is the most253demanded attribute and thus, it alone accounts for 25 percent of the rent. In contrast, landfilling attribute reduces house rent the most254by around 43 percent of the rent.255In planned areas, ventilation and landfilling lead to the most value addition and reduction to house rent contributes equally at 33256and 38 percent of the house rent, respectively. On the other hand, close proximity to open space boosts house rent by 11 percent257while waterlogging reduces it by 21 percent. In unplanned areas, households also value ventilation the most accounting 11 percent of258average rent while landfilling reduces rent by 40 percent. House rent, in unplanned areas, is also reduced by around 25 percent for259waterlogging though open space is not highly valued as it accounts only 4 percent of rent. From Table 4, we further found aggregate260negative value of environmental dis-amenities are much higher than the aggregate positive value of environmental amenities in the261urban housing market in both planned and unplanned in the Khulna city. This is due to the fact that residents are more sensitive to the262exposure of environmental dis-amenities than to the benefit of amenities. Hence, any presence of dis-amenities would lower down263house rent. Therefore, environmental improvement through good drainage and waste management system would yield net value264addition on housing rent by USD 66 per month for entire Khulna city while USD 75 and 52 for planned and unplanned areas,265respectively.2664. Conclusion267In this study, we estimated the value of environmental (dis)amenities in the housing market of Khulna city. In this regard, we268adopted a widely used hedonic pricing approach. To our best knowledge, this is the first attempt to investigate the contribution of269environmental attributes on house rent in Khulna city of Bangladesh. It is also a comprehensive study, which includes two en-270vironmental amenities, ventilation and proximity to open space and two (dis)amenities, waterlogging and landfilling around the271houses, to explain the variation in rents after controlling important structural and neighborhood attributes.272The study reveals that environmental amenities increase house rents by 5.4 percent for one-unit improvement in 5-point ven-273tilation scale and by 2 percent for every 100-m reduction in the proximity to open space while (dis)amenities reduce rents by 3.7274percent and 9.8 percent for one-unit degradation in 5-point waterlogging and landfill scale in Khulna city. Segregated results show275that ventilation is the most valued environmental attributes in both planned and urban areas since it is one of the scarcest attributes276in rented houses. On the other hand, adverse effect of the landfill by waste is much higher than that of waterlogging on house rent in277both planned and unplanned areas as waterlogging is a seasonal problem. More specifically, good ventilation accounts for 25 percent2781 The co-efficient for open space proximity for entire sample is −0.0002. This implies increase in rent 0.02 percent for 1-m reduction in proximity.279Therefore, for 100-m reduction in proximity, the increase in rent is 2 percent.280Md. S. Islam, et al.281Journal of Urban Management 9 (2020) 180–190282188283