COVID’s Impact on the Housing Market
The COVID19 outbreak is rapidly changing the housing market, with real estate companies quickly adopting digital strategies and alternative methods of doing business. In Ontario, for example, agents have been forced to change. The Real Estate Council of Ontario (RECO) has reported that, while real estate services are still considered essential, the government prohibited open houses as of April 4, 2020. Firms now have to sell digitally if they hope to attract buyers. Moreover, the agents with the most foresight in the business may also not be migrating online as a temporary measure – many expect the industry to change permanently after things settle down.
Reporting through major news networks such as the CBC, Toronto Star, Morningstar and others, real estate analysts seem to agree that the outbreak will impact housing prices as well. Toronto Real Estate Board (TREB) data showed that for the first 17 days of April 2020, MLS-based sales dropped 69 per cent compared to the same period last year. “Since COVID-19 forces a change in how people buy and sell, and the volume at which they do so, there is likely to be an expected change in housing market price behaviour,” says Amir Madadi, CEO of Gnowise.
The Gnowise intelligent home valuation system
Madadi and his team believe they can help. Gnowise offers a complete, neighbourhood-focused home price forecasting and valuation tool. They are also offering adjustments buyers can make based on their own preferences and experiences with the price. Complete with risk simulations under various conditions, Gnowise has grown its user base substantially since launching in 2018. The firm isn’t planning to cut out the experts either – which many AI/ML systems aim to do. Madadi says that “human interaction is the largest driver of trust in the markets – whether real estate or financial. And without trust, the market collapses. People often forget that when building complex models and API access for developers.” For that reason, Gnowise also offers a “human-in-the-loop” approach, with certified appraisers verifying AI/ML valuations from their platform.
That approach is proving particularly valuable right now, where traditional algorithms don’t take “unforeseen” risks into account the same way that experts do, he says. While Gnowise uses extensive amounts of open data to model prices off of every variable available, including distance to schools, macroeconomics, capital markets, jobs and so on, it can also adjust when needed.
Gnowise’s CTO, Faraz Arbabi, a PhD graduate of Engineering at the University of Toronto, specializing in numerical and computational modelling, says that this allows the firm to rapidly gather accurate, real-time data while researching COVID19’s impact on the housing market. “We’re in early stages of researching the exact price changes that occur due to the new shifts in the economy. We’re integrating more data every day, through the Centre of Excellence in Next Generation Networks (CENGN) Cloud Computing system based out of Communitech’s Data Hub. Soon, we expect to be able to forecast such impacts through our automated API.” Learn more about Gnowise here, and join them in the next CENGN cohort.
Using the Gnowise API
To use the Gnowise API, users must first obtain an API key provided by the firm, which can be requested along with pricing terms here. Note, your API call should pass information associated with a property. Gnowise then returns results associated with that property in the API response in less than one second. All Gnowise API calls are HTTPS POST requests. The response is JSON data contained in the body. An Intelligent Valuation Models (IVM) API sample can be found below:
Request:
POST /api/session-query HTTP/1.0
Host: https://pwlq74oz0j.execute-api.ca-central-1.amazonaws.com/gnowise-ivm-api Content-Type: application/json
x-api-key: [client key]
Content-Length: 116
Body: {“address”: “99 Cordella Ave”, “unit”: na, “zip”: “M6N2J8”, “bedrooms”: “3”, “washrooms”: “3”, “den”: 0, “age”: “15”, “rooms_area”: “2200”, “lot_area”: “3100”, “community_code”: “01.W03.0360”, “parking_spcs”: “1”, “pool”: na, “type1”: “Semi-Detached”, “style”: “2-Storey”, “basement1”: “Finished”, “basement2”: “Sep Entrance”, “kitchen”: 1, "condition": "3" "chartID": "e239d259-3837-4c6d-898e-1a59994888c3",}
Response:
{ "gnowise_value:": “8733400”, "value_low": "8177600", "value_high": "8733400, "attributes": [“location”, “bedrooms”, “den”, washrooms”, “age”, “rooms_area”, “type1”, “basement1”],
"price_forecast":[{"d":"2020-04-30T00:00:00.000Z", "v":8733400.0 }, ... ],
"best_time_to_buy": "2020-06-30 00:00:00", "best_time_to_sell": "2020-04-30 00:00:00", "risk_of_decline": 55.0,
"chartID": "e239d259-3837-4c6d-898e-1a59994888c3"}
Required Input Parameters
The table below lists required API Input parameters (for IVM):
| Parameter Name | Type | Descriptions |
| org_id | CHAR 32 | A unique client ID generated by Gnowise for your organization |
| bedrooms | INT 16 | Number of bedrooms |
| washrooms | INT 16 | Number of washrooms |
| den | INT 16 | den |
| age | INT 16 | Age of property |
| rooms_area | FLOAT 16 | Sqft of the property |
| community_code | CHAR 32 | Code of the neighbourhood |
| parking_spcs | INT 16 | Number of parking spaces |
| type1 | CHAR 32 | Type of property |
| style | CHAR 32 | Style of property |
| Basement1 | CHAR 32 | Type of basement 1 |
| Basement2 | CHAR 32 | Type of basement 2 |
| kitchen | CHAR 32 | Number of kitchens |
| chartID | CHAR 32 | Chart ID |
Recommended Input Parameters
The table below lists recommended API input parameters, which will enable Gnowise to analyse inputs in an optimal manner and will facilitate search functionality in the Gnowise control portal. Please note that it is a requirement to pass at least one account identifier as part of the API call.
| Parameter Name | Type | Descriptions |
| address | CHAR 128 | Property address |
| postal_code | CHAR 16 | Number of bedrooms |
| unit | CHAR 16 | Number of bathrooms |
| pool | CHAR 32 | Type of pool if exist |
| condition | INT 16 | Condition of property |
API Output Parameters
The Gnowise API result can return up to 10 different attributes.
| Parameter Name | Type | Descriptions |
| gnowise_value | INT 16 | Predicted price |
| value_low | INT 16 | Minimum predicted price value |
| value_high | INT 16 | Maximum predicted price range |
| attributes | ARRAY | Attributes effective on price |
| price_forecast | ARRAY | Price forecast |
| best_time_to_buy | DATE | Best date to buy this property |
| best_time_to_sell | DATE | Best date to sell this property |
| risk_of_decline | FLOAT 16 | Risk of price decline |
| chartID | CHAR 32 | Chart ID |
