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PropTech 2017: Smart Property Pricing

Posted on 06 Apr 2017

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Smart Property Pricing Jeremy Lee


Slide02

Sam: When we go to Cold Storage, it’s easy to identify the price of a steak. In this case it’s $12.25 or $8.90 per kilogram.

Slide03 
When we buy a pair of heels online or in a boutique, we know the price. In this case its $1,750 for a pair of Christian Louboutin shoes.

Slide04

When we buy a stock, we know its cost at that exact moment.

In all three cases – steak, shoes, and stocks – you either pay the price or walk. There is no negotiation.

Slide05

Real estate, on the other hand, doesn’t come with a price tag.

In the old days, we’d watch the neighborhood. I can remember sitting at the kitchen table with my sister and parents. My mom would say to my dad, “Did you see the Jones’s house is for sale?”

Slide06

“No, how much?”

“$43,000.”

“Wow,” my dad would say. “That’s a lot. They won’t get that much but if they do, maybe we should think about selling our house. Our house has gotta be worth more than the Jones’ home.”

Jeremy:  No we don’t do that anymore. Now we use big data, machine learning algorithms, technology-enhanced field inspection apps, and valuation control consoles.

The problem with the type of pricing you describe is that it is emotionally-based rather than based on the fundamental value of the home.

Every seller thinks his house is worth a sky-high price and every buyer thinks the listing price is too high. They both can’t be right so someone must help them establish the Right Price.

Sam:  And the best person to do that is the real estate agents through the negotiation process?

Slide07

Jeremy:  Yes, that’s correct. But the problem with relying solely on the agent to do the negotiation is that the agent has an inherent conflict-of-interest.

As we know from Freaknomics, the seller agent is incentivized to underprice the property to sell it quickly while the buyer agent is incentivied to convince the buyer to offer the listed price so the deal gets done.

Sam:  So what you’re saying is that there is built-in, structural conflict in pricing a home.

The buyer wants a low price.

The seller wants a high price.

The buyer agent has a bias towards the listed price.

And the seller agent has a bias towards compromising on the listed price.

So how do we solve this conflict?

Jeremy:  By introducing the type of pricing rigor we find in the stock market and pairing it to professionals.

Sam:  So everyone has a role here. The computer provides the foundation and benchmarking for professionals to make decisions on listing price, offer, negotiations, valuations for mortgage underwriting, valuations for legal and liability reasons, etc.

Jeremy:  Correct. Technology makes it possible to increase the rigor of property pricing by making the benchmarking more accessible while, at the same time, making it less costly for market participants to take advantage of this computer benchmarking.

Slide08

Sam:  And that is really the key point. Technology saves times, reduces cost, and empowers professionals to focus on the main objectives rather than scurrying around processing paper.  

As the above graphic suggests, technology has made property pricing so inexpensive that the average buyer and seller can avail themselves of professional services that were only available to big institutions in the past.

Jeremy:  Exactly.

Sam:  Price discovery, valuations, pricing, negotiations, and mortgage underwriting are complicated processes. There are many things taking place in the background that are not obvious to the public.   Put it all together for us.

Slide09

Jeremy:  As this diagram shows, there are many parts at play in the property pricing ecosystem.

It starts with raw data. The more the better. We call this Big Data.

It comes from multiple government agencies that have reinvested tax revenue to gather public information from property buyers and owners.

Also, it comes from market participants.

For example, the data points of a listing is an example of raw data that comes from the participant. As soon as an agent uploads a listing to a property portal, that unit data is now private-generated data that is now in the public domain and, according to the legal system, reclassified as public data.

Sam:  Raw data is available on many websites. For example, all property portals and HDB and URA show raw transaction data. Why isn’t that good enough?

Jeremy:  I like to compare it to rice and sushi. Raw data is like rice. Data that’s available on an application is like sushi.  It is much more interesting.

Slide10

Eating sushi at a fine Japanese restaurant at Jiro’s in the basement of Tokyo Station is a much different experience than wolfing down plain rice at home on a weekday night.

Sam: It’s a lot more expensive. At least $300 US dollars/person.

Jeremy:Yes. It’s more expensive because Jiro has added value to his rice and raw fish. We do the same thing with big data and applications.

Slide11

We clean the data and organize it so that it preserved and only used when it is relevant.

Sam:  That’s an important point, isn’t it? Raw data available on, say HDB website, can be misused because there is no quality control over the user.

Jeremy:  Correct. Jiro follows a strict process for handling his raw ingredients and then preparing the meal.

 Slide12

If they mishandle the rice or the fish, they will compromise the final product.

We are very careful in how we handle raw data because we cannot compromise the data’s integrity.

We have what is called ISO 9001 procedures that govern our processes and document our algorithms.

Sam:  Computers leave an audit trail.

Jeremy:  Also, unlike humans, they don’t forget. We use machine learning and unlike my daughters who are in school, they don’t forget what they learn.

Slide13

Going back to the ecosystem, we then use applications to turn the raw rice into suchi.

We have seven primary applications:
  1. Price indices,
  2. X-Value,
  3. Valuation Field App,
  4. SRX V-8 Full Valuation,
  5. X-Listing Price,
  6. Mortgage Underwriting, and
  7. SRX Analyzer.

Humans then uses the applications to make decisions on valuations, Goodwill, Listing Price, Offer, Counter-offers, which leads to the negotiation and close, mortgage underwriting for financing, investment choices, analysis for government policy, and the regulation that comes out of that policy.

Sam:  Let’s take just one of those decisions: the listing price, which, if is done according to the rigor of this pricing ecosystem, is called X-Listing Price.

Slide14

Jeremy: Every listing price starts with X-Value. It is computer-generated and objective. It does not have any emotion and only looks at the facts of a property. Things like location, size, bedrooms, etc.

Sam:  How robust is X-Value?

Jeremy:   Highly.


Slide15

When we compare X-Value to what actually people transact, we see a normal distribution of people paying + / - 3% of the X-Value.

Now let’s take a look at Private Condo:


Slide16

Here is HDB:

 Slide17


Here is Landed:


 Slide18

 

Sam: So X-Value is a great starting point?


Slide19


Yes, but it is not enough because transacting a home requires matching a willing buyer with a willing seller and a computer cannot account for the intrinsic value of a home – let’s call it the emotional value.

Slide20

This emotional value is part of the Goodwill which takes into account all the other reasons one would pay above the fair market value of the property, like future expectations of capital appreciation.

There is also a role for the valuer to take into account other subjective factors such as View, Orientation and Renovations of the home. To do so, the valuer will have to do a site inspection.

Slide21

And technology makes it much easier, faster and cheaper to do a traditional valuation. Field inspection app auto-populates most of the data for the valuation.  As such, the valuer's job is to follow procedures for a field inspection, verify computer's data, and write an opinion. There is no need to use a Word document to type out reports as the computer uses a template with electronic signature, making it possible for valuations to be out within hours from site inspections.  By improving productivity and efficiency of a valuer, average market participants can now afford to use valuations for listings, negotiations and bank loans. 


Slide22


Sam:
It looks like X-Value also keeps the Valuer’s work honest.


Jeremy:

There might be an incentive for valuers to match valuations with transactions.

We see that in the data after HDB made the COV rule change in Mar 2014.

The number of exact matches between valuation and transacted prices jumped more than 1,300%, from 3.7% of all transactions to close to 50%.

 Slide23

Sam:  Why do you think there is such a high percentage of matching?

Jeremy:  We recently did a study on this and the data suggests that the fees for HDB valuatons are so low that there might be less incentive for valuers to do an independent valuation.

Also, buyers and / or sellers may be putting tremendous pressure on valuers to match the transaction price to secure financing.

We can’t know the reason for sure, but certainly the incentives are misaligned for the market participants.  This should be a regulatory concern since HDB regulation requires valuations to take place after the Offer-to-Purchase. Technology can easily solve this.

Sam:  Speaking of HDB, I heard Minister Lawrence Wong mentioned that DIY transactions in the HDB resale market doubled from 11% to 24% since 2010. What do you make of it?

Jeremy:  I was surprised when I first heard of these figures as well.


Slide24


So I ran a study based on SRX transactions and found that most people, especially journalists, have probably misunderstood what the Minister said.

The proportion of HDB transactions using at least one agent hasn’t changed in the last 6 years.

I have since clarified with HDB and they defined DIY from the perspective of the buyer, not the transaction.  If there was no buyer agent, they would classify it as DIY.

Sam:   So that means not all the DIYs previously reported are DIYs the way we think of a DIY, which we define as having no agent involved.

Jeremy:  Correct. Most reported DIY have a seller agent but are missing a buyer agent.

So the jump from 11% to 24% is mainly the drop off of a buyer agent, not seller agent.


Slide25

This is likely due to CEA’s rule prohibiting dual representation – which prohibits an agent from acting as both buyer and seller agent.

Sam:  In the time that is remaining, let’s talk briefly about the role of computers and humans in valuations. Andrew Chee will talk about this in more detail during the roundtable discussion on Smart Professionals, but I wanted to ask you how you view the computer-human relationship.

Jeremy:  The purpose of the computer in a valuation firm is to cut costs and improve efficiencies without compromising the integrity of the field inspection and the valuation.

 

Slide26 

As you can see from the SRX V-8 Full Valuation System, the computer does all the grunt work of collecting, cleaning, and organizing the data used in a valuation. This means the valuer can spend his or her time on high-value tasks.

Later in the program, we will show the field app as well as the Chief Valuer console. These systems allow a valuation firm to handle higher volumes of business and conduct field applications faster.

Higher volumes of business leads to improved margins and some of those margins can be passed onto clients and partners in cost-savings, lead generation, and other efficiencies.


Slide27

Sam:  And that is what technology is all about: empowering the individual to deliver better service and make more. Thank you for this discussion on Smart Pricing.

 

 

To download the powerpoint slides of Smart Property Pricing, click here

F
ind out more about on srx.com.sg/proptech

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