International Property Tax Institute

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SINGAPORE – September 2019

WE BUILT A MACHINE LEARNING MODEL THAT CAN ESTIMATE PROPERTY VALUES IN SINGAPORE ...................... 1

DOWNTOWN SINGAPORE IS A NEW PROPERTY PLAYGROUND.............................................................................. 5

We built a machine learning model that can estimate property values in Singapore Estimating a property’s value can be a tricky business. You could look at its features (size, year built, or available amenities), compile the neighborhood’s statistics (population or median household income), or monitor economic indicators of the country’s housing market. But this leaves out what all property investors know is the most crucial driver of value: location. Put a Starbucks or a high-end hotel nearby, and our intuition tells us this could increase the demand for the surrounding area. However, placing a multi-million dollar bet on a property is risky, and investors want to base their decisions on data. Luckily, we now have a wealth of geospatial data at our fingertips. Nontraditional sources like OpenStreetMap, social media activity, and satellite imagery provide up-to-date, granular data that can be powerful predictors of property value. The challenge is then to sift through the millions of data points available to find what matters most and use these to identify potentially profitable investments. Finding property hotspots with machine learning Using a machine learning model that we built on open-source geospatial features, we were able to predict Singapore real estate prices with 87% accuracy (i.e., within an error margin of S$100). Additionally, we could use the model to estimate the price per square foot across the city-state by looking at property features such as the distance to major roads and points of interest (POIs) like restaurants and hotels.

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International Property Tax Institute

IPTI Xtracts- The items included in IPTI Xtracts have been extracted from published information. IPTI accepts no responsibility for the accuracy of the information or anyh opinions expressed in the articles.

Grabbing data from the Urban Redevelopment Authority of Singapore’s open listing of apartment and condominium sales over the last four years, we compiled a dataset showing the locations and unit price per square foot for over 50,000 transactions. We then used OpenStreetMap to find available POIs in Singapore. These include night clubs, train stations, traffic signals, and road types. With the help of Geomancer, an open-source library for geospatial features that we created in-house, we transformed the points of interest into quantifiable features, such as the distance to the nearest taxi stop or the number of restaurants within a 1 kilometer radius. After splitting the dataset and setting aside 30% of the listings for testing, we used the remaining 70% of the properties and their geospatial features to build the machine learning model. Some of the features with the greatest predictive power included: the postal district and the distance to the nearest hotel and the nearest primary road. For example, compare the features of luxury apartment Boulevard Vue, which is right off Orchard Road, to the D’Leedon apartment complex near Farrer Road:

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International Property Tax Institute

IPTI Xtracts- The items included in IPTI Xtracts have been extracted from published information. IPTI accepts no responsibility for the accuracy of the information or anyh opinions expressed in the articles.

Although the apartments are similar in size and are in the same postal district, Boulevard Vue is much closer to a hotel, primary road, ATM, and a fast food restaurant. Accordingly, it’s almost five times more expensive than the D’Leedon apartment.

To test how well the model works, we compared its predictions to the actual selling price of each property. We found that it performs well on median prices, but tends to underestimate properties which are extremely cheap or expensive – most likely because there were fewer examples of these in the test dataset.

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International Property Tax Institute

IPTI Xtracts- The items included in IPTI Xtracts have been extracted from published information. IPTI accepts no responsibility for the accuracy of the information or anyh opinions expressed in the articles.

Breaking down the model, over half of the predictive power comes from the postal district where a unit is located. That’s not surprising since postal districts give good insight into the relative desirability and level of development of an area. (For comparison, District 9 in central Singapore has an average price of S$2,200 per square foot, while District 17 at the easternmost tip of the island, has a going rate of S$900 per square foot.) Within each district, we can further improve our price estimation by 40% if we add in the distance to POIs. Creating this model shows that we can come up with accurate, granular estimates using only one dataset (Open Street Map) which is easily accessible via an API – no need to consolidate data from multiple sources or do research on each building or developer. Nevertheless, unit-specific features may be one way to refine our model further. Right now, all the units inside a building or city block have identical geospatial features, making them have the same estimated price. Information like floor area, floor level, year built, or even building facilities can be integrated into the model to capture variations between units. While this kind of data is more difficult to collect at scale, it may be worthwhile if it leads to significant improvements in accuracy. Potential impact Machine learning models like these can support two strategic directions for real estate investors or developers: It identifies the next property hotspots in underused but high-value areas for acquisition and development. Instead of having a team of analysts collect and compile reports based on aggregate (and possibly outdated) numbers, the model can automatically collect and process real-time data to quickly find opportunities that others may miss.

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International Property Tax Institute

IPTI Xtracts- The items included in IPTI Xtracts have been extracted from published information. IPTI accepts no responsibility for the accuracy of the information or anyh opinions expressed in the articles.

It gives developers a data-driven approach to discover what features actually increase property value. In Singapore’s case, POIs like hotels and restaurants are key. For other markets, the model can be retrained given the right dataset. Developers can then assess the POIs in a given area and come up with a strategy to increase their properties’ overall value. It’s become increasingly obvious that big data is the future of real estate. Companies that can leverage these technologies now will be able to translate data into actionable insights at an unprecedented speed and scale to get ahead of their competitors.

Downtown Singapore Is a New Property Playground Singapore’s property industry is coming to terms with the idea that, in about a decade, the city may no longer have a central business district. Landlords couldn’t be more chuffed. There will still be offices, but without the rigid boundary between places where people work, and where they live and shop and dine. Local developer GuocoLand Ltd. is among the first to run with the Urban Redevelopment Agency’s draft blueprint for a makeover of Singapore’s landscape. The company’s S$2.4 billion ($1.8 billion) Midtown project, a two-minute drive from City Hall, will have a 30-floor office tower, a 32-story apartment block and about 32,000 square feet of retail. The design of the condominium units will be unveiled next week. Expect more such projects, as well as the redevelopment of as many as 20 office towers. The government wants old office buildings near the central bank and the stock exchange to make way for mixed-use properties. The new structures will be allowed 25% to 30% higher development intensity, meaning their owners will have more space to sell or rent out. The incentive could nudge City Developments Ltd. to tear down its old but centrally located Fuji Xerox Towers or City House, though as of last month’s earnings call CEO Sherman Kwek was yet to make a decision. Globally, city managers are starting to consider whether core downtown areas have outlived their 20th-century purpose of offering high-quality offices in an attractive urban environment. On those two criteria, Singapore is at or near the top of the league tables, ahead of London’s City and Canary Wharf, Marunouchi in Tokyo, New York’s Financial District, and La Defense in Paris, according to a 2017 study by Ernst & Young and the Urban Land Institute. But when it comes to attracting and retaining talent – the No. 1 attraction of a business district, according to the property investors, developers and tenants in the EY-ULI survey – Singapore lags all its main rivals, except Tokyo and Hong Kong.

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International Property Tax Institute

IPTI Xtracts- The items included in IPTI Xtracts have been extracted from published information. IPTI accepts no responsibility for the accuracy of the information or anyh opinions expressed in the articles.

This is why the Asian city-state wants to re-purpose the downtown as “not only a place to work, but also a vibrant place to live and play in.” The hope that there will be a constant flow of resident traffic on weekends – when the office area is otherwise deserted – is putting an extra zing even into shophouses, Singapore’s original mixed-use buildings built between the 1840s and the 1960s. A row of six such conservation terraces, with tenants including an insurance firm, a bath-ware brand and a Middle Eastern restaurant, is currently on sale at an indicative value of about S$58 million, the Business Times reported Tuesday. Their selling point is the 3%-plus rental yield because of demand induced by the government’s urban master plan. All this is good news for property owners, especially the real-estate investment trusts that Singaporeans favor for their steady payouts amid low yields on other investments. Retail and hospitality REITs have much to gain from the remaking of the central business district. As I’ve written previously, new leases for CapitaLand Mall Trust, Singapore’s biggest shopping-center landlord, are strong only in suburban neighborhoods. There’s hardly any rental increase from three years ago in the marquee shopping districts of Orchard Road, Clarke Quay and Bugis, where GuocoLand’s Midtown project is coming up. Livening up the underused downtown won’t entirely protect physical retail from assault by e-commerce, but it can slow the decline of brick-and-mortar stores.

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International Property Tax Institute

IPTI Xtracts- The items included in IPTI Xtracts have been extracted from published information. IPTI accepts no responsibility for the accuracy of the information or anyh opinions expressed in the articles.

Ultimately, the newly created housing stock will need to be filled. If the ongoing anti-government protests in Hong Kong intensify, Singapore may get a natural flow of new residents, though a 20% stamp duty on foreign buyers – introduced to tame speculative purchases – will make them more likely to rent than own. A hawkish stance on immigration, amid a declining birth rate, implies that the population in 2030 will be significantly below the 6.9 million figure projected in a 2013 government white paper, which had sparked worries about overcrowding. Any hint that Singapore is taking its feet off the brakes on immigration would make REIT investors ecstatic, though with a change in political leadership in the cards, no such bonus can be expected soon. For now, builders have to make more downtown apartments, hoping people will come.