# RECORD 7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8 # TITLE: NET BENEFITS OF URBAN SLUMS REDEVELOPMENT POLICIES ON THE VALUE OF ADJACENT RESIDENTIAL PROPERTY # YEAR: 2021.0 VENUE: nan DOI: 10.3846/ijspm.2021.14570 # AUTHORS: Touseef Hussain; Wei Zou # PAGES: 12 CANDIDATE TABLE PAGES (1-based): [7, 8, 9] # ABSTRACT: Over time, the government of Pakistan introduced several strategies to deal with the issue of slums in its capital city, Islamabad. Contrary to the traditional strategies that primarily focus on the tenure security of occupants, the strategy of slum redevelopment is more composite. It involves the up-gradation of slums in their existing sites as well as the demolition of slums from the posh areas. The present study is first to empirically investigates the outcomes of the slums redevelopment policies on the value of adjacent residential properties. Using a rental valuation based hedonic price model, the study examined the spillover effects of an urban slum up-gradation and eviction projects on the nearby housing values. The results show that the redevelopment of slums reduces the adverse effects on residential value. It is essential for the urban planners to cautiously include the slum redevelopment projects spillover effects on the adjacent property value in different geographical locations, to maximize the net positive effects of improvements on the neighborhood. ===== PAGE 1 (score 8, numeric-paren 1, table-words 0, var-words 7) ===== Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unre- stricted use, distribution, and reproduction in any medium, provided the original author and source are credited. NET BENEFITS OF URBAN SLUMS REDEVELOPMENT POLICIES ON THE VALUE OF ADJACENT RESIDENTIAL PROPERTY Touseef HUSSAIN, Zou WEI* College of Public Administration, Nanjing Agricultural University, 210095 Nanjing, China Received 06 May 2020; accepted 20 January 2021 Abstract. Over time, the government of Pakistan introduced several strategies to deal with the issue of slums in its capital city, Islamabad. Contrary to the traditional strategies that primarily focus on the tenure security of occupants, the strategy of slum redevelopment is more composite. It involves the up-gradation of slums in their existing sites as well as the demo- lition of slums from the posh areas. The present study is first to empirically investigates the outcomes of the slums rede- velopment policies on the value of adjacent residential properties. Using a rental valuation based hedonic price model, the study examined the spillover effects of an urban slum up-gradation and eviction projects on the nearby housing values. The results show that the redevelopment of slums reduces the adverse effects on residential value. It is essential for the urban planners to cautiously include the slum redevelopment projects spillover effects on the adjacent property value in different geographical locations, to maximize the net positive effects of improvements on the neighborhood. Keywords: slums, up-gradation, demolition, hedonic price model, property value, Pakistan. Introduction The fast-growing urbanization has consistently been a dis- tinctive characteristic of the process of development and has almost been continuously accompanying by the simi- larly fast growth of informal settlements, squatter settle- ments, slums, and other uncontrolled settlements (Asian Development Bank and Inter-American Development Bank, 2014; Malik et al., 2020). Slums had always been considered an essential component of cities globally since the outgrowth of urbanization and cities of Pakistan is not an exceptional case; the slums are locally known as Katchi abadis. Governments around the world have recognized the issue of slums as many economic, social, and envi- ronmental problems are linked with the creation of slums (Nijman, 2008). In Pakistan, the government has taken several steps to resolve the issue of slums. Over time, the policies of the state have gone through paradigm shifts from emphatic demolition to the provision of housing units to the urban poor. Currently, in Islamabad, there are almost 34 slums out of which the government owns only ten slums as authorized and takes responsibility for the development of infrastructure (Hussain et al., 2019). The government rehabilitated the slums using the three con- cepts application of Urban Renewal, which includes slums Up-gradation at the present site in existing form, Slums relocation to the Model Urban Shelter Project (MUSP), and Slums Up-gradation at the site in the form of planned plots. In comparison, the rest of the slum settlements are unauthorized or illegal and faced eviction or demolition over time (Malik et al., 2020). The current study attempts to investigate the outcomes of several government initia- tives for the improvement of urban infrastructure and the redevelopment of slum areas. One of the critical arguments about adopting urban redevelopment is that it brings positive externalities to the real estate in the neighborhood (Newell, 2010). Further- more, redevelopment supports the improvement process of the urban environment, boosts the property values in the locality, and improves life standard, public security, and overall image of the city. Supporters of urban renewal believe that redevelopment results in a win-win situation for all the stakeholders by creating positive externali- ties beyond the borders (Newell, 2010). Moreover, they consider redevelopment as an essential tool for bringing positive externalities to augment the values of the prop- erty (such as land and building) for the owners of the property, whereas increasing the chances for authorities in raising the tax base. A few studies also depict that re- development schemes on a large scale enhance the prop- erty values in the neighborhood, such as (De Sousa et al., International Journal of Strategic Property Management ISSN: 1648-715X / eISSN: 1648-9179 2021 Volume 25 Issue 3: 228–239 https://doi.org/10.3846/ijspm.2021.14570 *Corresponding author. E-mail: zw@njau.edu.cn ===== PAGE 7 (score 67, numeric-paren 13, table-words 18, var-words 12) ===== 234 T. Hussain, Z. Wei. Net benefits of urban slums redevelopment policies on the value of adjacent... 4. Results and discussion Tables 4, 5, and 6 show the estimations of the regressions function of existing slum areas, up-gradated slums, and the evicted slum sites, respectively. These tables have re- sults of both linear and log-linear or semi-log functional forms of the model. For the estimation of the effects of existing slums, upgraded slums, and evicted slum sites on house rental value, the study used three indicators: distance to existing slums, distance to upgraded slums, and distance to evicted slum sites. Thus, each table has two models, and each table is with different indicators, whereas each model is assessed with both semi-log and linear specifications. 4.1. Effects of existing slums on the rental price The values of the adjusted R2 are from 0.9416 to 0.9504 in the log-linear and linear model that shows the model’s independent variables can explain around 94 to 95 percent fluctuation of the dependent variable and be able to indi- cate the models goodness of fit. The results of the linear model demonstrate that the house located a distance of one kilometer away from exist- ing slums enjoys more rental value of PKR 5894. It means the housing units with the same attributes located one kil- ometer closer to slum areas lost their rental value around PKR 5894 for existing near to slum areas. On the other hand, the log-liner model results also reveal rent price discount for the dwellings situated within a kilometer dis- tance from the slum sites, housing units lost almost 18.6% rental value for being located nearby slum areas as com- pared to those similar houses located a kilometer away from such slum sites (see Table 4). Furthermore, dwelling units located very close to slum sites are affected the most, the visibility of the nearest slums from the house also de- creases the rental value by around 15%. 4.2. Effects of slum up-gradation on the rental price Table 5 presents the regression results estimation of both linear and log-linear models. General, the signs of the co- efficients seem as expected, however statistically few vari- ables are not significant. The R2 (demonstrates the sum of the fluctuation in the dependent variable due to independ- ent variables) is 93% in the linear model and 91% in the Semi-log or log-linear model, which is highly satisfactory. The results show the gap between residential housing units and up-graded slum areas are positively correlated with each other, as distance increases from the up-graded slum sites, the rental prices also increase. Meanwhile, the rental prices decrease as distance decreases from the up- graded slums. The distance to the up-graded slum site, we observe that its parameter estimate is positive and highly significant statistically at the 99 percent level in both mod- els. The coefficient estimated with the semi-log functional form shows that the housing unit located 1 km away from the up-graded slums, rental prices of residential housing units increase by between 12.5% (see Table 5). That means houses located one kilometer closer to up-graded slums lose the rental value of 12.5% as compare to those housing Table 4. Presenting estimates of a linear and log-linear model with data from existing slums Variables Linear model Log-linear model Coefficients Std. Err. t-value Coefficients Std. Err. t-value House age 66.18976* 33.64222 1.97 .0018017* .0008823 2.04 House unit size 79.99556** 4.551541 17.58 .0013917** .0001194 11.66 Bedrooms 1690.173** 554.19 3.05 .0420453** .0145349 2.89 Drawing room 2549.089** 852.2414 2.99 .0834143** .0223519 3.73 Bathrooms 734.8915 620.7361 1.18 .0181466 .0162802 1.11 TV lounge 2003.42* 953.2602 2.10 .087841** .0250014 3.51 Garage 977.9888 707.9799 1.38 .0537868** .0185684 2.90 Servant quarter 2646.275** 671.7168 3.94 .0585302** .0176173 3.32 Lawn 1815.562** 679.3289 2.67 .0913645** .0178169 5.13 Distance to CBD 1113.781** 142.3801 7.82 .0122385** .0037342 3.28 Distance to school 1324.727 1594.824 0.83 .0751735 .0418278 1.80 Distance to park 3562.901** 999.7477 3.56 .1086817** .0262206 4.14 Distance to slums 5894.832** 1632.232 3.61 .1864864** .0428089 4.36 Visibility 5985.225** 1228.731 4.87 .151787** .0322262 4.71 _cons 14133.57** 2084.127 6.78 9.837591** .0546609 179.97 R-squared 0.9547 0.9466 Adj R-squared 0.9504 0.9416 Prob > F 0.0000 0.0000 Note: *, ** denotes significance at 5% and 1% levels. ===== PAGE 8 (score 55, numeric-paren 13, table-words 10, var-words 16) ===== International Journal of Strategic Property Management, 25(3): 228–239 235 units situated a kilometer away from up-graded slum ar- eas. Furthermore, the linear model shows PKR 4921, and the log-linear model reveals a 10.8% less rental price for housing units close enough to visible to up-graded slums. 4.3. Effects of slum eviction/demolition on the rental price Table 6 represents the estimation results of the eviction of slum areas’ effects on house rental value by using linear and log-linear models. The satisfactory level of adjusted R2 of both models demonstrates the model’s goodness of fit. The distance to the evicted slum sites (the critical vari- able in this model) is associated with an increase in rental price at a rate of 6.7% per kilometer, keeping other vari- ables in the model constant. That means houses situated a kilometer closer to evicted slum sites have a discount on the rental price of 6.7%. The findings of the current study reveal the significant positive effect of both the up-gradation and eviction of slum areas on the residential housing rental value. It sug- gests that slum up-gradation and eviction of unauthorized katchi abadis has brought improvements to the neighbor- ing environment, and the public anticipation regarding the advantages of improved environment and amenities trigger the appreciation of the residential prices. The find- ings of the current study are somehow comparable with the findings of some urban development studies such as De Sousa et al. (2009) and Ki and Jayantha (2010) even though Zhang et al. (2016) study are about evictions of inferior settlements (urban villages) on residential prices. However, the comparability needs to be done much care- fully as the redevelopment of slums is unmatchable with the previous research. The evidence of a study conducted by Zhang et al. (2016) found the net benefits of the evic- tion of the urban villages on residential property prices in Beijing, China. The study showed that an average urban village negatively affects residential prices of around 2.5% of nearby housing societies, and the removal or demoli- tion of an urban village enhances the value of neighbor- ing housing up to 3.4%. The study showed lower adverse effects of urban villages on the residential properties as compared to slums or katchi abadis. The reason for this is might be because of the difference in characteristics of ur- ban villages and katchi abadis. The urban villages in China are different from the slums in Pakistan. Urban villages are formal settlements with tenure security, and the govern- ment is responsible for the development. In contrast, slums or katchi abadis are informal set- tlements and do not have land ownership rights, and the government is not responsible for the development. The situation in katchi abadis is more deteriorated and causes several socio-economic, political, and environmental is- sues as compared to the urban villages. The evidence of the study conducted by De Sousa et al. (2009) on the im- pact of the redevelopment of Brownfields on the residen- tial value in Milwaukee and Minneapolis, United States, revealed the net benefit of urban brownfield redevelop- ment increased the residential property value by around Table 5. Showing estimates of a linear and semi-log model with data from upgraded slums Variables Linear model Log-linear model Coefficients Std. Err. t-value Coefficients Std. Err. t-value House age 166.016** 45.90104 3.62 .0045487** .0010464 –4.35 House unit size 65.67229** 4.548889 14.44 .0009076** .0001037 8.75 Bedrooms 2041.083** 718.7292 2.84 .0488116** .0163849 2.98 Drawing room 3378.79* 1378.357 2.45 .067677* .0314224 2.15 Bathrooms 894.3884 678.3305 1.32 .0122106 .0154639 0.79 TV lounge 1652.742 1157.746 1.43 .0591468* .0263932 2.24 Garage 1545.144 1140.948 1.35 .0643446* .0260102 2.47 Servant quarter 3936.82** 1136.299 3.46 .088549** .0259043 3.42 Lawn 2333.943* 1089.434 2.14 .0558746* .0248359 2.25 Distance to CBD 4085.165** 808.8975 5.05 .1300239** .0184405 –7.05 Distance to school 2238.825* 1076.843 2.08 .0511055* .0245488 –2.08 Distance to park 2816.003* 1211.215 2.32 .086923** .0276121 –3.15 Distance to upgraded slums 6568.099** 2070.675 3.17 .1258162** .0472052 2.67 Visibility 4921.391** 1451.89 3.39 –.1080642** .0330988 –3.26 _cons 38553.44** 6267.595 6.15 10.82699** .1428826 75.78 R-squared 0.9441 0.9193 Adj R-squared 0.9392 0.9121 Prob > F 0.0000 0.0000 Note: *, ** denotes significance at 5% and 1% levels. ===== PAGE 9 (score 61, numeric-paren 14, table-words 11, var-words 18) ===== 236 T. Hussain, Z. Wei. Net benefits of urban slums redevelopment policies on the value of adjacent... 2.7% in Minneapolis and 11.4% in Milwaukee. The net benefit of the redevelopment of brownfields in the Mil- waukee region is nearly matching with the net benefit of the eviction of katchi abadis of the current study. The functions of regression in Tables 4, 5, and 6 dis- play the figures, as well as the estimated sign of neigh- borhood and structural characteristics in existing slum sites, up-gradated slums, and the evicted slum site data, are as expected in all model specifications of both linear and log-linear models. The log-linear model showed more significant results as compared to the linear model. The study more precisely included the results of the log-linear specification to explain the structural and accessibility variables of the study. The variables for physical attributes of dwellings have the anticipated coefficient signs and are mostly coherent with the outcomes of immense literature of hedonic modeling. For instance, the additional bed- room surges the rental value of the house by 4.2% in the existing slum site. Nevertheless, the enhancement in rental price due to the existence of an additional bedroom is 4.4% in the case of evicted slum site areas. The presence of a servant quarter in the house increases the rental value by up to 5.8% in the existing slum areas and 8.8% in the up-graded slum areas. It is 7.9% in the evicted slum site. The size of the dwell- ing unit is the most influential independent variable to forecast the rental price, with the highest t-value of 11.66, 8.75, and 12.01, respectively, among all other independent variables. Against anticipations, the additional bathroom does not significantly affect the rental price in all data sets of the log-linear model. Furthermore, the presence of ga- rage or parking space was given equal importance by both models, with an approximate t-value of 2.90 in the existing slum sites, 2.47 in the up-graded slum sites, and 2.33 in the evicted slums sites data set. The results also show that the presence of lawn enhances the rental price by approxi- mately 9% in existing slum sites, 5% in up-graded slum areas, and 2% in evicted slum sites data set (see Tables 4, 5, 6). The presence of public facilities enhances the rental value of nearby houses, distance increases between public services and housing units significantly reduces the rental value of residential property in the market. Conclusions and policy implications The study aimed to examine the value enhancement im- pact of urban slums improvement and redevelopment pol- icies on the rental prices of residential property. This study used slum up-gradation and eviction projects to evaluate this issue empirically. The results of the study depict that redevelopment resulted in substantial value enhancement to neighboring residential property. Notably, the rental prices of residential units in the vicinity of evicted and upgraded slum sites have been increased significantly. The results suggest that potential house residents seem more willing to spend for a house that is situated nearer to the redeveloped locality. Table 6. Showing estimates of a linear and semi-log model with data from evicted slum sites Variables Linear model Log-linear model Coefficients Std. Err. t-value Coefficients Std. Err. t-value House age 71.68437 38.28094 1.87 .0013215 .0010493 –1.26 House unit size 77.49149** 5.64242 13.73 .001857** .0001547 12.01 Bedrooms 1596.682** 413.7285 3.86 .0443812** .0113403 3.91 Drawing room 950.7497 585.3236 1.62 .033505* .0160437 2.09 Bathrooms 951.5119* 460.7668 2.07 .0222714 .0126296 1.76 TV lounge 1137.778* 511.8009 2.22 .0327429* .0140285 2.33 Garage 1375.326* 527.7794 2.61 .0337009* .0144664 2.33 Servant quarter 2524.987** 631.1266 4.00 .0794629** .0172992 4.59 Lawn 1034.311* 502.4433 2.06 .0295001* .013772 2.14 Distance to CBD 809.7306* 330.3623 2.45 .0184054* .0090552 –2.03 Distance to school 1809.456** 688.3523 2.63 –.0361071 .0188677 –1.91 Distance to park 2949.794** 1098.344 –2.69 –.0778371* .0301056 –2.59 Distance to evicted slums 2930.03* 1490.882 1.97 .0792828* .0408651 1.94 Visibility 2237.263* 1077.658 –2.08 –.0676483* .0295386 –2.29 _cons 20617.66** 3801.181 5.42 10.05659** .1041903 96.52 R-squared 0.8817 0.8683 Adj R-squared 0.8709 0.8563 Prob > F 0.0000 0.0000 Note: *, ** denotes significance at 5% and 1% levels. ===== PAGE 10 (score 16, numeric-paren 0, table-words 0, var-words 15) ===== International Journal of Strategic Property Management, 25(3): 228–239 237 From these findings, we can draw various conclusions. Firstly, for landlords and developers, an enhanced envi- ronment neighborhood quality can undoubtedly be seen as a vital selling point. Those slum development projects which are well planned and implemented are capable of enhancing the environment of the vicinity and support the marketability and value of surrounding properties. Hence, an enhanced environment of the vicinity is like a guarantee or promise from the authorities of urban de- velopment. Therefore, the residential property values will reach to highest standards. Finally, it helps to surge the competitiveness and marketability of the properties. Consequently, the authorities of urban development and planning possibly will consider the actual benefits that slum redevelopment creates in the market in particular and the society in a broad-spectrum. Secondly, the aware- ness of people about the real benefits of the slum redevel- opment will be enhanced. The positive perception of the general public creates a favorable impression of the slum’s redevelopment in the society at large. Consequently, the negative attitudes in particular segments of society about slum redevelopment will be changed. The findings of the current study indicate specific nec- essary implications. Considering policymaking bodies, this study highlights the main underlying assumptions about the implementation of the slums redevelopment process: the advantages received by the nearby real estate market because of improvement in proximate slum set- tlements. It is due to the slum redevelopment capability for enhancing the environment of urban living, hence in- creases the life quality, city image, and safety of the public. Additionally, it also helps to improve the property values in the vicinity that will result in an increase of tax base for relevant authorities. Though slum redevelopment projects incur high costs, developers and authorities think that the measures taken for raising public awareness about the slum redevelopment will be a business opportunity for them. It could also assist in enhancing redevelopment policies and implications and increasing redevelopment demand in the market. Moreover, the findings could also help bring the atti- tude of Islamabad to improve the environment at a higher standard. Due to a lack of awareness of the potential ben- efits of slum redevelopment, several segments of society are reluctant to support the slum area’s redevelopment. So these findings will also be helpful for those sections and authorities to understand the real benefits of slum rede- velopment. This will help authorities to make decisions when to go for redeveloping a slum area as it should be taken place when the worth of property/land of a new expansion surpasses the worth of properties outstanding in its present use. The present study has some limitations which need to be acknowledged. The lack of housing sales transaction data is one of the limitations of the study. The function of hedonic price modeling is based on the self- reported rental price of housing units, related research is usually done with the sale price of the residential unit. The study is limited to the influences of slum improve- ment or redevelopment policies on the residential prices only; the studies in the future may include the value of properties in the commercial and industrial areas. Hence, further research requires more precise data for testing and consolidating the findings of this study. Acknowledgements Authors are thankful to College of Public Administration, Nanjing Agricultural University (NJAU). Funding The APC was funded by Nanjing Agricultural University, China. 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