Property
Better data access needed for property planning
V Sanjugtha 
Imbalances were also glaring in the retail and office sub-sectors with unoccupied private office space standing at 3.4 million sq m
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IN an era of voluminous information, how did the domestic property market arrive at a gross mismatch between demand and supply of homes?

When people are in dire need of a place to live, there should not be a surplus beyond the average Joe’s means.

Yet statistics show that in Q1 last year, total unsold residential properties stood at 130,690 units – the highest in a decade.

This is close to double the historical average of 72,239 units per year between 2004 and 2016.

Data from the National Property and Information Centre (Napic) also show about 83% of the total unsold units were in the above RM250,000 price category.

Johor’s 27% is the largest share of total unsold residential properties. This is followed by Selangor at 21% and Kuala Lumpur (14%).

Similarly, imbalances were also glaring in the retail and office sub-sectors with unoccupied private office space standing at 3.4 million sq m.

Some 2.79 million sq m of space in the retail sector was also unoccupied as at June last year. Yet 38 million sq ft of office space and 44 mil sq ft of retail space are set to flood the market in the next three years.

 

Information deficit

Why is there a disconnect between meeting society’s housing needs and the bottom line requirements of property developers?

Despite statistics reporting a steadily rising overhang in property sub-sectors such as the office market, and retail and commercial assets, it seems incredulous that its proliferation persists.

Ultimately, market observers are pointing at lack of information available to developers as the prime culprit for the asymmetry plaguing the property industry.

While data exists from a multitude of sources, not many can afford to fork out the hundreds of thousands of ringgit required to purchase the data needed to make good and informed decisions.

A property observer tells FocusM that Napic charges 60 sen per raw data, for data on a house transaction.

He says this translates to huge amounts to be forked out to obtain sufficient data to analyse and determine trends in real estate development decisions.

This, he claims, is beyond the affordability of most small to medium organisations in the country.

“The problem we face in the property market is that the relevant bodies having the information appear to be making money from the information gap in the market.

“Also, note that the data are valid for six months only,” he says.

Acknowledging Napic’s annual Property Market Report, which is available to the public for a small fee, he points out that it is too outdated to be of relevance.

Bank Negara Malaysia (BNM) recently announced its five-pronged strategy to address the affordable homes conundrum, outlining the need for an integrated database as a key feature to combating the bane.

Malaysia lacks a database that captures the supply and demand of housing which incorporates information such as household income, characteristics and preferences.

This stands in the way of ensuring the supply of homes meet the requirements of demand.

 

Lack of market studies

The property observer agrees and says the crux of the overhang in the market is largely the result of a lack of proper studies.

“The problem is there is insufficient data to conduct market feasibility studies as information such as income levels, household income and absorption rate are not readily available.

“What developers conduct are price and product comparison surveys and a planning report, rather than a proper market feasibility,” he says.

The absence of demand data hinders an in-depth study, fuelled by the lack of coordination between the different local councils despite the close boundaries.

Oftentimes, policymakers are not able to make evidence-based subsidy decisions, as they rely on inadequate housing demand and supply data, says Harrison

In the recent World Urban Forum 9 organised by BNM and the World Bank, Dao Harrison, a senior housing specialist at the latter who serves markets in the Asia Pacific, addressed the role of data in the growth of the real estate market.

In her delivery at a panel discussion entitled Navigating the affordable housing market in urban cities, she observes that the country lacked information on demand-side data indicators.

“Oftentimes, policymakers are not able to make evidence-based subsidy decisions, as they rely on inadequate housing demand and supply data,” Harrison says.

She warns that this can lead to mistargeted subsidies that fail to reach the intended sector of the population due to the housing cost, location, or the subsidy design among other factors.

 

Lower prices

Harrison believes that better access to data could lead to actions and interventions that could improve the price structure for affordable housing.

Access to robust housing data can reduce information asymmetry in the market and create a situation where buyers and sellers can transact independently and agree on a fair price without the interference of third parties.

A transparent market ecosystem will automatically ensure that government programmes and interventions target the right households and in large numbers.

Harrison says the environment of transparency it creates would be mutually beneficial to all parties and enable governments to fine-tune housing policies and subsidy programmes.

This ensures more efficient use of the fiscal subsidy budget by having real-time data on the housing stock and deficit as well as household composition, needs, demands, and constraints.

Better access to more robust data will also allow governments, investors, developers and researchers to identify housing needs and gaps in the housing value chain by location, income segment, and household type for better planning, investment and provision of affordable housing.

Additionally, it will provide data for the private sector or researchers to innovate through the creation of open source housing platforms that can subsequently benefit the public sector and allow governments to simulate housing policy scenarios before they are implemented.

Harrison points to the need for certain core housing indicators related to quantitative and qualitative housing deficits.

This, she says, is what the World Bank found notably absent from the websites of relevant government housing agencies such as the Ministry of Urban Wellbeing, Housing and Local Government, Napic, and the Department of Statistics Malaysia (DOSM).

She elaborates that the quantitative housing deficit or backlog of homes, can be calculated as the number of households less the number of existing housing units, plus the number of new household formation less the number of estimated housing stocks.

Harrison says housing needs must be differentiated from housing demand, which is generally influenced by personal preferences, the capacity to pay and supply housing, and price points to name a few.

On the qualitative housing deficit indicators, Harrison says they refer to housing improvement or extension needs related to sub-standard housing due to poor construction quality, lack of basic services such as water, sanitation and electricity or over-crowded living conditions.

She stresses the importance of tracking the quantitative and qualitative deficits need by income segments and geographical areas which would enable governments to set more precise housing targets.

Harrison says without the housing deficit data, it is difficult to determine the effectiveness of the volume target the government has set in its 11th Malaysia Plan to address the actual housing deficit needs.

She was referring to the 11th Malaysia Plan (2015-2019), which outlines a target to provide 606,000 units of affordable houses for low- and middle-income households and 47,000 units to be constructed or repaired for the poor.

There is a gross mismatch between demand and supply of homes in the domestic property market


Interactive database

The World Bank strongly recommends the development of an interactive database, which is a real estate database platform with open access to public and private sectors.

However, it stresses the importance of ensuring an appropriate setting to protect consumers’ privacy and data protection.

Harrison notes that creating such a platform is no easy task. She says that in Malaysia, data integration is a key challenge as they are currently owned by different government agencies (Napic, Construction Industry Development Board, BNM, DOSM, etc).

“There is a need for one single entity that takes on the task of coordination and integration and more importantly, of cleaning and triangulating to enhance accuracy and analyse data to make sense of it all.

“As the private sector is well advanced with the use of new technology and analytical capacity, governments might consider leveraging private sector capacity or joint ventures in developing a Housing Data Knowledge Centre,” she says.

She also points out that the use of big data and technology has already transformed the real estate private sector.

The public sector can benefit tremendously by amplifying its housing data with other big data sources. This will enable far-reaching planning capacity.

Harrison believes the country has a strong platform for collecting data on new housing supply and transactions via Napic, mortgage data via BNM and household income and segmentation data via the DOSM.

However, she notes that rental-related data also need to be strengthened.

The property observer agrees that data is available via the various government agencies, and there is a need for a singular one to coordinate the availability of data for housing in the public domain.

However, he points out that there is a dire need to ensure all bodies involved have a uniformed definition of such data.

“At present Napic, for example, only captures formal housing data.

“There are other government agencies that account for informal homes and government quarters, which causes inconsistencies in the data gathered,” he says.

He also notes the need to ensure the parameters for data collection are consistent to ensure the quality is not sub-par.

The quest for a centralised database in Malaysia, he believes, can only be led by the government.

Hence, he calls for all related government agencies to come to a consensus on defining data, setting the parameters, and liberalising what is collected into the public domain.

“In Singapore data is readily available to the public.

“But here, we need to pay to obtain data, which is fine if it’s a nominal sum. But that’s not the case and it’s exorbitant.

“To liberalise data and ensure a robust database is created, the initiative must be led by the public sector with buy-in from all stakeholders obtained,” the property observer stresses.

Harrison notes that there are several best practices that the government could evaluate to model for further development of its housing information system, namely Canada’s National Mortgage and Housing Corporation.

It maintains a Housing Market Information Portal with up-to-date statistics for local, regional, and national housing markets, including housing construction, rental and homeownership market prices, population and households, and housing stock.

Another model is the Thai Real Estate Information Centre (REIC) which provides indexes for housing and house rental rates, statistics for land allocation and housing construction permits, construction commencement information, real estate sales and housing completions.

Better access to data could lead to actions and interventions that would improve the affordable housing pricing structure in Malaysia

Using applications to predict housing trends

Housing applications are increasingly being used to predict trends and gauge demand and location preferences among dwellers.

“From our perspective [World Bank], integrated database alone is not enough and neither is technology.

“What is sufficient beyond integrated database is the application of technology,” says Dao Harrison, its senior housing specialist.

She describes an integrated database as “flat” and says there is a need for applications that predict trends, directions, insights and understanding so stakeholders can make the right decisions.

Harrison notes that several countries use creative applications to make predictions, for example using data from census, transportation, social media and Google to provide a feeling of what it’s like to be living in a particular area.

This application provides information on types of transportation available, parks, bakeries and sports facilities. It also incorporates safety and security measures employed in the area as well.

She cites the Café Index, which is a predictor of house price trend correlated to the number of cafes that have mushroomed in a neighbourhood as an example.

The Café Index was developed by Cha-Ly Koh, CEO of Propertypricetag.com in her coffee-table book The Secret Atlas of Greater KL.

The book attempted to understand the people of Greater Kuala Lumpur and their quirky behaviours through the lens of data.

The Café Index found that the number of cafes has a positive correlation to property prices in the surrounding area.

Neighbourhoods with a high number of cafes are also those where property prices are most likely to rise.

In China, Harrison says there is an application that uses big data from Alibaba.com and Hangzhou Municipality to marry landlords and tenants with a high assurance level, owing to advanced usage of data.

Landlords are able to review the financial capacity of potential tenants while the application called Smart Rental Housing allows tenants to ensure the property is actually owned by the former.

In the US, the Housing and Urban Development Department created a Location Affordability Index (LAI) that models the estimated percentage of household income spent on housing and transportation to identify locations where affordability is strained, using the yearly household survey estimates and satellite imagery.

Planners can then see affordability discrepancies at the neighbourhood, city, and regional level. This tool allows policymakers and developers to analyse where to increase affordable housing investments. 

World Bank’s Open Trip Planner

Having an open housing platform with data collected from the public and private sectors, and researchers and academics opens a universe of possibilities for analysis and innovation.

One of the best examples of open data in a different market is Google Maps, a private initiative that uses public transit data to provide real-time information on transportation around the world, helping users find efficient routes that lower their time and transportation costs.

A housing-specific example is The World Bank’s Open Trip Planner Accessibility Tool.

This open-source platform uses algorithms to calculate households’ accessibility to jobs, education, health centres, and other key urban infrastructure within a 60-minute public transit ride.

It allows users to assess the optimal location for future affordable housing development projects by locating parcels within accessible transit and urban infrastructure nodes.

 



This article first appeared in Focus Malaysia Issue 279.