ENGAGEMENT BRIEF Improving the Analytical Capacity of the Indonesia’s Housing and Real Estate Information System BASIC INFORMATION GSCP SUPPORT OVERVIEW PROJECT NAME National Affordable Housing Program TITLE Support the Development of the Housing and (NAHP P154948) Real Estate Information System GLOBAL PRACTICE Urban, Disaster Risk Management, Resilience OBJECTIVE To provide analytical support to the NAHP’s and Land Housing and Real Estate Information System (HREIS) technical assistance activity, REGION East Asia Pacific including (i) integration of housing data into a COUNTRY Indonesia unified database, (ii) development of housing indicators and dashboard, and (iii) preparation GEOGRAPHICAL National of a technical report on affordable housing SCOPE market trends, constraints and opportunities. COUNTERPARTS Ministry of Public Works and Housing SUPPORT PERIOD July 2021-March 2022 GSCP INPUT US$ 18,000 (expert time), 3 virtual missions OUTPUT • Technical report on affordable housing market trends, key indicators, and dashboard design • Presentations on housing data INDONESIA fundamentals with key housing indicators and housing information of a smart city • Contribution to the NAHP Implementation Support Mission Aide Memoire www.worldbank.org/gscp 2 CONTEXT and data sharing between these institutions are currently Indonesia is undergoing a major and rapid structural limited. Further, even though relevant housing data have transformation into an urban manufacturing- and service- been collected by various institutions, they have not been based economy. About 158 million people – 57 percent of optimally utilized due to the lack of the methodology for data Indonesians – live in cities and towns in 2020 and by 2046, analysis, and thus good analytics are not readily available approximately 220 million people – over 70 percent – will live when needed. In this context, the World Bank team asked the in cities. Urbanization has the potential to be a major driver Global Smart City Partnership Program (GSCP) to provide of prosperity and inclusiveness in Indonesia, but realizing technical support and guidance on data collection and this potential requires bold institutional reforms. The rapid establish a robust approach to producing and coherently growth of urban areas has put pressure on infrastructure, presenting timely, reliable and serviceable data analytics, basic services, land, housing, and the environment; it has also trends, and indicators. eroded the livability of cities and reduced the prosperity gains from urbanization.1 The large increases in urban population, APPROACH combined with a dearth of affordable housing options for To more effectively integrate and analyze various housing- lower-income households, has led to overcrowding and the related data and develop housing indicators, GSCP experts expansion of slums characterized by substandard housing, reviewed the existing HREIS website and dashboard, as well inadequate access to basic services, poor health, and as analytical works conducted by the HREIS team. They then vulnerability to disaster risks.2 provided inputs to make the dashboard more informative and user-friendly. Given the Ministry of Public Works and Housing The Indonesian government has developed a broad set of (MPWH) privacy policy, the HREIS team was not able to policies and institutions to support affordable housing, provide all required data to the experts, and thus only sample but these have not yet been effective in improving housing data was provided. As such, GSCP experts were not able to conditions at scale. An efficient housing delivery requires fully analyze specific data, which limited the depth of the access to an integrated database and key housing team’s recommendations. Further, all the data fields were in indicators that allow both the public and private sectors to Bahasa Indonesia, which made it challenging for the experts assess housing needs and find the most cost-effective and to understand the data directly. Nevertheless, GSCP experts structurally and technologically efficient ways to fulfill those searched for a flexible way to use the dataset, while the World needs. In particular, access to reliable and timely housing- Bank team helped with translating key data and information. related data and indicators can help the public sector make In particular, continuous discussions were held with the HREIS informed regulations and policy decisions and better target team and Indonesian officials on housing stock and price, government housing subsidy programs. and household reference, among others. It is noteworthy that The World Bank is financing the National Affordable Housing the HREIS team produced a cookbook detailing how they Program (NAHP) in support of the Indonesian Government’s developed the system. They also conducted a separate survey ambitious “One Million Homes” Program (Satu Juta Rumah), to estimate the intention of owning a new home among which aims to provide adequate and affordable housing for Indonesians. Both the cookbook and survey enabled GSCP all Indonesians. Under the project, the Government initiated experts to provide more useful advisory services. in November 2019 the development of a Housing and Real The experts generated a technical note addressing the Estate Information System (HREIS)3, which aims to establish main challenges in developing countries and proposing an integrated database and information system to guide methodologies of measuring housing affordability and housing policies, programs, and decision-making that can availability, focusing on low-income groups, and assessing help improve the Indonesian housing market as a whole. The housing needs using housing classifications. They system is designed to serve the needs of all relevant housing recommended key indicators to be analyzed as well as sample stakeholders and will initially focus on affordable housing. and mockup analytics using random data, to demonstrate what analyses using certain indicators would entail. They CHALLENGES also provided guidance and inputs and introduced methods of The lack of transparent, spatially-tagged information analyzing housing density, affordability, and burden as well as on land and home prices and their legal status is a major other key indicators related to housing demand and supply. barrier preventing municipal governments from undertaking critical large-scale urban-based Public-Private Partnerships The GSCP experts, MPWH, and HREIS team met virtually at affordable housing projects. HREIS is essentially a Big Data different stages of implementation in order to understand project that aims to serve as a repository of reliable and the client demand and data system’s status, monitor the up-to-date consumer housing needs and demand. Its goal HREIS development progress, and discuss recommendations is to offer housing and real-estate data on both supply prepared by the GSCP experts. In the end, the recommended and demand sides, including pricing and spatial data, by indicators from the cookbook and analytics were incorporated consolidating housing data that are currently scattered into the HREIS dashboard. across different institutions in Indonesia. Coordination 3 FIGURE 1. RECOMMENDED KEY INDICATORS FOR THE HREIS DASHBOARD HOUSING STATISTICS Housing status Housing needs assessment • Housing tenure (e.g., rate of ownership, owner-occupied, etc.) • Housing status by housing cost burden • Duration of stay • Housing status by income group • Housing level of service • Forecast affordable housing gap • Housing Area per Capita Housing sale • Housing Supply Ratio • Number of housing sales - Housing-to-Housing Ratio: number of housing units per • Number of new home sales number of household • Housing price (index) - Housing-to-Population Ratio: number of housing units per number of people Housing construction Housing inventory characteristics • Housing permits • House by price (sale price, rent) • Housing starts • House by size, number of rooms, number of bathrooms • Housing construction cost • House by service (water, sanitation, electricity, gas, heating, Finance internet, etc.) • Housing investment • House by form (single-detached, multi-unit, apartment, etc.) • Mortgage/interest rate • House by structure (reinforced concrete structure, steel structure, wooden structure, etc.) • Outstanding mortgage • House building characteristics (plot size, building coverage • Loans/non-performing loans ratio, floor-area ratio, floors, parking lot, earthquake Population and economy resistance, etc.) • Population (residents characteristics) • House by level of service (formal & satisfactory, formal & substandard, informal & sub-standard, etc.) - Population trend and projections • House by housing supplier (e.g., private, public, - Population (change) by birth, death, inflow, and outflow subsidized, etc.) - Population (change) by age • Housing vacancy ratio - Population or household/family (change) by income (decile) Housing cost burden analysis - Household trend and projections • Housing cost burden by personal/household/family income - Household by type (e.g., single, couple, parent with child, etc.) • Housing cost burden by percent of expenditure on housing - Household by the number of members payment - Household by household head type (e.g., father, mother) • Housing cost burden by age and age • Housing cost burden by household size - Household by income and expenditure • Economy - Gross domestic product (GDP), gross regional domestic product (GRDP), GDP per capita, GRDP per capita - Job change - Employment rate - Saving rate - Poverty rate 4 RESULTS LESSON LEARNED Support from the GSCP has enhanced the HREIS Developing a system like HREIS is a commendable dashboard development for user interface as well as achievement in its own right. But this engagement analytics. The recommendations were able to guide the demonstrates that developing a system for digitizing data HREIS team in analyzing different indicators from a is one thing; producing insightful analytics that can guide housing perspective instead of an IT-based approach, public and private decision-making processes is another. enabling the team to produce robust analyses of the key Critically, by pairing a competent team of system developers indicators to support evidence-based decision-making in with housing sector experts, this engagement filled the gap in the housing sector. The engagement experience with the housing analytics and serves as a major lesson for other cities Indonesian client in developing HREIS was shared in other and countries considering a similar system. countries such as Mongolia. MOVING FORWARD Specifically, during the implementation of the HREIS HREIS can serve as a platform to integrate housing data from technical assistance, one of HREIS’s features (Housing Queue) different cities and provide them with the required data from was socialized and piloted in several cities to assess the relevant stakeholders. As with any system, HREIS will also effectiveness of the housing queue system in determining benefit from continuous enhancement, specifically to collect the list of priorities for housing support, such as identifying more data and produce analyses that can provide insights on the households that most urgently required it. Through housing challenges and potentials to support governments prioritization based on data, the government is expected to in crafting evidence-based policy and programs, developers improve the targeting of government housing programs and and lenders in making investment and lending decisions, demonstrate how data systems increase the efficiency and and consumers in making purchase and sale decisions. The effectiveness of housing planning, which is aligned with the first step ought to be clarifying who will be responsible for smart city program. managing the HREIS dashboard in the long run. The Global Smart City Partnership Program (GSCP) started in 2018 to help World Bank Group teams and clients make the best use of data and technologies for improving city planning, management, and service delivery. This engagement brief was prepared based on a desk review of a GSCP completion report, field travel reports, presentations, technical notes, and other project outputs, as well as selected interviews with the World Bank Group teams. Roberts, Mark, Frederico Gil Sander, Sailesh Tiwari. 2019. Time to ACT : Realizing Indonesia’s Urban Potential. Washington, DC: World Bank. 1 https://openknowledge.worldbank.org/handle/10986/31304 2 World Bank. 2014. Development Policy Review 2014: Indonesia: Avoiding the Trap. Jakarta: World Bank. 3 https://hreis.pu.go.id/portal_hreis/ Photography: Shutterstock, Page 1: AsiaTravel, Page 4: Bagus upc