Dataset Information

Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches.

ABSTRACT: Housing value is a major component of the aggregate expenditure used in the analyses of welfare status of households in the development economics literature. Therefore, an accurate estimation of housing services is important to obtain the value of housing in household surveys. Data show that a significant proportion of households in a typical Living Standard Measurement Survey (LSMS), adopted by the Word Bank and others, are self-owned. The standard approach to predict the housing value for such surveys is based on the rental cost of the house. A hedonic pricing applying an Ordinary Least Squares (OLS) method is normally used to predict rental values. The literature shows that Machine Learning (ML) methods, shown to uncover generalizable patterns based on a given data, have better predictive power over OLS applied in other valuation exercises. We examined whether or not a class of ML methods (e.g. Ridge, LASSO, Tree, Bagging, Random Forest, and Boosting) provided superior prediction of rental value of housing over OLS methods accounting for spatial autocorrelations using household level survey data from Uganda, Tanzania, and Malawi, across multiple years. Our results showed that the Machine Learning methods (Boosting, Bagging, Forest, Ridge and LASSO) are the best models in predicting house values using out-of-sample data set for all the countries and all the years. On the other hand, Tree regression underperformed relative to the various OLS models, over the same data sets. With the availability of abundant data and better computing power, ML methods provide viable alternative to predicting housing values in household surveys.

SUBMITTER: Embaye WT

PROVIDER: S-EPMC7877618 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches.

Embaye Weldensie T WT Zereyesus Yacob Abrehe YA Chen Bowen B

PloS one 20210211 2

Housing value is a major component of the aggregate expenditure used in the analyses of welfare status of households in the development economics literature. Therefore, an accurate estimation of housing services is important to obtain the value of housing in household surveys. Data show that a significant proportion of households in a typical Living Standard Measurement Survey (LSMS), adopted by the Word Bank and others, are self-owned. The standard approach to predict the housing value for such ...[more]

PMID: 33571198

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Project description:BackgroundLinking facility and household surveys through geographic methods is a popular technique to draw conclusions about the relationship between health services and population health outcomes at local levels. These methods are useful tools for measuring effective coverage and tracking progress towards Universal Health Coverage, but are understudied. This paper compares the appropriateness of several geospatial methods used for linking individuals (within displaced survey cluster locations) to their source of family planning (at undisplaced health facilities) at a national level.MethodsIn Malawi, geographic methods linked a population health survey, rural clusters from the Woman's Questionnaire of the 2015 Malawi Demographic and Health Survey (MDHS 2015), to Malawi's national health facility census to understand the service environment where women receive family planning services. Individuals from MDHS 2015 clusters were linked to health facilities through four geographic methods: (i) closest facility, (ii) buffer (5 km), (iii) administrative boundary, and (iv) a newly described theoretical catchment area method. Results were compared across metrics to assess the number of unlinked clusters (data lost), the number of linkages per cluster (precision of linkage), and the number of women linked to their last source of modern contraceptive (appropriateness of linkage).ResultsThe closest facility and administrative boundary methods linked every cluster to at least one facility, while the 5-km buffer method left 288 clusters (35.3%) unlinked. The theoretical catchment area method linked all but one cluster to at least one facility (99.9% linked). Closest facility, 5-km buffer, administrative boundary, and catchment methods linked clusters to 1.0, 1.4, 21.1, and 3.3 facilities on average, respectively. Overall, the closest facility, 5-km buffer, administrative boundary, and catchment methods appropriately linked 64.8%, 51.9%, 97.5%, and 88.9% of women to their last source of modern contraceptive, respectively.ConclusionsOf the methods studied, the theoretical catchment area linking method loses a marginal amount of population data, links clusters to a relatively low number of facilities, and maintains a high level of appropriate linkages. This linking method is demonstrated at scale and can be used to link individuals to qualities of their service environments and better understand the pathways through which interventions impact health.

Project description:BackgroundThe Affordable Medicines Facility-malaria (AMFm) is primarily an artemisinin combination therapy (ACT) subsidy, aimed at increasing availability, affordability, market share and use of quality-assured ACTs (QAACTs). Mainland Tanzania was one of eight national scale programmes where AMFm was introduced in 2010. Here we present findings from outlet and household surveys before and after AMFm implementation to evaluate its impact from both the supply and demand side.MethodsOutlet surveys were conducted in 49 randomly selected wards throughout mainland Tanzania in 2010 and 2011, and data on outlet characteristics and stocking patterns were collected from outlets stocking antimalarials. Household surveys were conducted in 240 randomly selected enumeration areas in three regions in 2010 and 2012. Questions about treatment seeking for fever and drugs obtained were asked of individuals reporting fever in the previous two weeks.ResultsThe availability of QAACTs increased from 25.5% to 69.5% among all outlet types, with the greatest increase among pharmacies and drug stores, together termed specialised drug sellers (SDSs), where the median QAACT price fell from $5.63 to $0.94. The market share of QAACTs increased from 26.2% to 42.2%, again with the greatest increase in SDSs. Household survey results showed a shift in treatment seeking away from the public sector towards SDSs. Overall, there was no change in the proportion of people with fever obtaining an antimalarial or ACT from baseline to endline. However, when broken down by treatment source, ACT use increased significantly among clients visiting SDSs.DiscussionUnchanged ACT use overall, despite increases in QAACT availability, affordability and market share in the private sector, reflected a shift in treatment seeking towards private providers. The reasons for this shift are unclear, but likely reflect both persistent stockouts in public facilities, and the increased availability of subsidised ACTs in the private sector.

Dataset Information

Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches.

Publications

Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches.

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