Blockchain: Setting a Data Precedent in Real Estate
It has been argued that access to language was the single most distinctive trait which enabled humans to exert their dominance over their current domain. Communication between individuals liberated ideas from the shackles of time and kick-started the procreation of ideas. Communication between groups cleared the path for empathy, gradually ensuring that the default option when meeting a new group wasn’t to slaughter them as benefits could be had from collaboration. Granted that nowadays, it seems that a shared language enables groups to better define how they are ‘different’ from the rest but at least this is carried out through inflammatory posts on Facebook rather than incendiary devices on people.
A shared language was what created cities but it was data standards underlying the internet which caused a Cambrian explosion of knowledge sharing. Early standards were eventually formalized into accepted protocols which form the backbone of how we communicate today (HTTP, IP, etc.). In fact, this is how society evolves as we constantly update the rules of society based upon shared ideas which works. Books required us to learn how to read and write; trade forced us to understand finance and accounting; the web demanded that we comprehend computers and smart devices. In all these cases, there is a steep learning curve but such practices eventually became second nature enabling society to focus on reaping the benefits: knowledge storing and retrieval from books; skill specialization enabled by trading; real-time knowledge sharing via the web. All of this is to say that data standards is the very first step that any industry will have to take in order to realize the true benefits of collecting such data in the first place.
This brings us back to the real estate industry (RE) which at the moment lacks the proper alignment of incentives to establish a strong data foundation. A major reason for this is that RE, by nature of it being a very physical industry, is significantly more context specific as compared to other assets. There is a reason why the everlasting motto in RE is “location, location, location”. Even taking a single factor such as gross floor area will have varying definitions from one market to the next. Adding to this is the myriad of units available: square feet, square meter, tsubo (Japan), ping (Taiwan), pyeong (Korea). Some markets and platforms even sidesteps this in favor of room count.
Given the increasing customer expectation gap for information-enabled RE businesses, blockchain technology might just be the catalyst needed to drive the industry forward. This is a bold claim which lacks any backing at the moment but as I argue here, blockchain technology is set to disrupt the power dynamics in the industry by realigning core incentives. Moreover, it provides the much needed alternative business models in an industry which has so often relied on capital controls of data and private networks to survive. How then, do we start to have an informed discussion around utilizing blockchain technology to better serve society as a whole?
Rubbish in, rubbish out: Laying the foundations
Establishing a well-defined data structure will minimize any headaches when it comes to dealing with any sort of data in the future. Of course, such a structure will also have to be flexible enough such that any widely accepted standards in the future will be able to be integrated with minimal disruption. This entails having a good understanding of the data as well as the requirements around any data that is currently being captured.
It must be noted that due to the heterogeneity of RE, it is impossible to gain a complete image of a particular property via info displayed on screens. Moreover, though the data collected might be similar, there is a significant difference in requirements depending on the type of property being analyzed (residential, office, alternative, etc.). In particular, commercial real estate data tends to have relatively higher levels of confidentiality requirements which contributes to higher levels of capital controls on data.
Due to the above, below are some unconventional ways of looking at RE data in order to inform a more practical approach to realizing the benefits of blockchain technology. Additionally, subtleties with blockchain tech (specifically cryptoeconomics) has been simplified so as not to over-complicate things. Such approaches are not mutually exclusive but rather a general framework to consider when trying to determine the protocols of the future.
- Bare minimum data required to continue business-as-usual (BAU): This is the most straightforward approach as it essentially takes the current data standards and migrates it to a distributed ledger. Though much benefit will be realized from doing just this, the system will likely be unsustainable as the system will likely be bogged down by unnecessary data which keeps growing in complexity. Moreover, in the cases where there is a need for confidentiality, such info will still need to be kept off-chain leading to the same inefficiencies brought about from siloed databases that we see today.
- Data abstraction based on data type requirements: Given the physical aspect of RE, there will be a fixed set of objective data that will always form the master data that the industry refers to. Moving forward, there is a need for a industry-wide consensus on what this master data list entails. This will enable only critical data to be written to the chain providing significant performance improvements. A major factor affecting the types of data stored on the shared ledger will be the confidentiality of such info as well as the ability of upcoming hashing solutions.
- Data required for consumers to make an informed decision: Much of RE is able to sustain themselves via extracting economic rent by selling access to opaque data. This is especially true in CRE but incumbents will have to weigh the risks of such rent dissipating as disruptors enter the space driven by an alignment of goals with consumer wants. As such, there will be certain data types which the market will demand more transparency around. Examples of this are maintenance records, transaction history, and valuation history.
- Data with real value add: Related to above, when there is sufficient structured public data available in the market, any additional data costs which the market is willing to incur will go into genuine data discovery. This could be a property study available for purchase via the main chain or a portfolio strategy piece requested by a particular company. This is the type of data where RE professional views are really needed rather than providing ‘teaser data’ in order to gain leads.
With this in mind, we can then start to look at the different data types in RE. Institutions such as RICS and OSCRE will form strong starting points for looking at the data types as each have been pushing the industry to adopt shared standards. Please note that the list below is non-exhaustive but rather a general guideline to organizing the amount of RE data. Additionally, it doesn’t cover the subtleties of databases such as nesting and mappings. The economics of maintaining such an updated blockchain is also not within the scope of this article but suffice to say, it will be a vital consideration.
- Location: This is information which, for the most part, is already publicly available with a widely adopted format. As such, there should be minimal resistance to moving such information to a public distributed ledger. In fact, many governments are currently footing the costs of maintaining such a directory.
[Unit, Level, Street, District, City, Country, Post Code, Administrative Area, Coordinates]
- Building: Such information forms the core data required for market making and is therefore largely publicly available although there are significant gaps. There might be some resistance to making such info publicly searchable as certain markets like to provide just the minimum amount of data required in order to generate a lead. This is also what contributes to the opaque network effects in the industry.
[Area, Usable Area, Use Type, Completion Date, Parking Allowance, No of Storeys, Ceiling Height, Amenities]
- Plant and Equipment: This applies more to CRE and specifically to the alternative markets. Such information is usually only available upon speaking to the tenants or service providers who have had experience in the specific building. Increased transparency here will greatly aid in reducing redundant middleman costs and delays. With that being said, there might be a need for access restriction when it comes to the incumbent’s equipment as business strategy can be inferred from such information.
[Type, Feature, Specification, Age, Maintenance Schedule]
- Infrastructure: Going by current market practices, such data is usually not requested save for specific business lines. Nonetheless, with relatively little to be made from keeping such info behind a paywall, having the option to access such data will aid in market matching.
[Communication, Water, Electric, Transportation, Capacity, Pipelines]
- Lease: This is likely where data transparency will face significant resistance, specifically around costs and lease conditions. Non-disclosure agreements between landlord and tenants are a common occurrence as such opacity protects the industry’s interest. Much of the redundancy (and therefore revenue) in RE are a result of digging out such intentionally obscured data. This is also what contributes to the limited downside of RE which is up for debate whether this is truly beneficial. Such opaque RE practices will be extremely hard to tackle but the industry’s response to such technological opportunities will expose whose interests they have at heart.
[Landlord , Tenant , Lease Term, Rent, Deposit, Developer, Agent, Occupation History, Contract]
- Services: This is another data type where there are interests seeking to maintain the status quo, specifically in CRE where such services are in high demand. Currently, for the large majority of cases, service providers respond to Request-For-Proposals by leveraging upon preferential rates obtained via established relationships with vendors. This is not necessarily a bad thing as service providers get rewarded for the convenience they provide. However, a consequence of this is that there is significant redundancy when it comes to data discovery as each response requires multiple rounds of clarifications (on both the client and vendor side) with no reference to previous agreements.
[Provider, Vendor, Service Agreements]
- Valuation: RE valuation is not exactly a science but is highly dependent on the rental rates for the building. In fact, landlords actually prefer a higher face rent (with higher tenant incentives) in order to get a better valuation for their asset. As such, although it will be less commonly referenced, it faces the same challenges when making such figures public. An alternative way which such info could be made available on-chain is by providing all previous valuation reports behind a paywall. This increases accountability in the industry as no single-party will be able to cherry pick their information or obscure unfavorable information.
[Currency, Method, Assumptions, Transactions]
- Legal & Compliance: Many governments already have a publicly searchable (either free or paywalled) official directory. There will be little confidentiality issues around this information hence the challenge around such data will be the integration/migration of databases. Having such a directory with the correct mappings between property and coordinates will minimize the due diligence costs and effort significantly.
[Registry No, Codes, Titles, Easements]
- Certificates: This is information which landlords will be promoting as it reduces tenant uncertainty. In fact, potential buyers or tenants will feel uncomfortable if such information is obscured. Blockchain technology has the added benefit here in that such certificates can be easily verified. As such, this data type should be relatively easy to implement.
[Fire, Safety, Environment, Energy, Design, Heritage]
- Other Files: For other files which do not fit the schema but is still relevant. Photos will be vital in market matching hence should have minimal confidentiality issues. Availability of floor plans will be a significant value add hence will likely be demanded by the market. Other files will be a value-add hence access should be made available if required. The pipe dream will be for the shared ledger to function as the central repository for all relevant property data.
[Floor Plan, CAD, Photos]
Baby Steps
There will be significant data gaps as the industry transitions into such a system but proper data standards will form the core building blocks for RE‘s evolution. Having this data (limited or not) on a shared, secured and performant ledger will minimize the redundancy in the industry, allowing the industry to drive real value for their users. This will also form the backbone for data from IoT devices to plug into. Following this, industry wide trends and deep-dives can be conducted via machine learning and artificial intelligence. The value-add to society will be immense but it all begins with baby steps.
Given how the majority of RE business lines have yet to go digital, this actually presents an opportunity for the industry to leapfrog technologies as it does not need to reinvent the wheel. Ideally, professional bodies or consortiums will have to lead this charge as there is little incentive for individual companies to adopt such tech if there isn’t a critical mass. The goal is to have a robust and flexible enough data infrastructure rather than a perfect schema from the start.
The above is definitely an oversimplification of the RE industry but the industry needs to start taking the first steps towards establishing a strong data foundation. Without such a base, concepts such as data mining, IoT, and artificial intelligence will remain just that. Sure, data analysis is possible if a company maintains their own database but it will become increasingly costly and time consuming to continually align and cleanse their own siloed database. These costs will continue increasing as we move into the data age as the inefficiencies of siloed databases accrue exponentially. Couple that with the increasing expectation gap between consumer and enterprise platforms and you’ve got a space increasingly ripe for disruption.
Thanks for staying till the end. Would love to hear your thought/comments so do drop a comment. I’m active on twitter @AwKaiShin if you would like to receive more digestible tidbits of crypto-related info or visit my personal website if you would like my services :)
