Open to housing, planning & data analyst roles · Kitchener, Ontario
Housing & Urban Data AnalystI'm Jiakang Zhang. I've been doing research and data analysis since 2021, first at a community mental-health nonprofit, now on how cities grow and change. I translate the results into plain language that decision-makers can act on.
Every project I work on follows the same arc: find the data, place it on the map, test what it really says, and explain it so anyone can use it.
I collect and merge data that doesn't want to be merged: census tables, scraped listings, transit schedules, map files, all pulled into one clean, analysis-ready dataset. Often with Python doing the heavy lifting.
With QGIS and ArcGIS I pin every data point to its neighbourhood, so trends stop being abstract numbers and start being places, street by street, block by block.
I build statistical and economic models in Stata that separate genuine effects from coincidence, then run the diagnostic checks to prove the findings hold up.
Briefing notes, dashboards, maps, and presentations in plain language. My communication of complex findings earned a Methodological Excellence Award.
Toronto had more than 14,000 entire-home Airbnb listings and new rules meant to keep them in check, but almost no one had measured whether either was changing what tenants pay. So I built the dataset and measured it.
Swap Airbnb for any policy question. Is the new zoning by-law changing permit volumes? Is the vacancy tax working? Where should the next transit dollar go? The pipeline stays identical: build the data, map it, test it, brief it.
A 135-page thesis is really eight jobs done back to back. Each card below is one of them. Click through to see how each was done.
Spotted what was missing: almost no Canadian evidence on Airbnb and rents, and none at all after Toronto's new by-law took effect.
See how → 02 · Data engineeringNo dataset existed, so I made one: 10 sources, from scraped rental listings to transit schedules, merged with Python and APIs.
See how → 03 · Spatial analysisPinned 20,000+ listings to their neighbourhoods in QGIS and computed real door-to-door transit times for every rental.
See how → 04 · Statistical modelingUsed an instrumental-variable design (2SLS), a standard econometric method, to separate Airbnb's real effect on rents from simple coincidence.
See how → 05 · Insight & impactCitywide, Airbnb activity is linked to rents about 5.6% higher than they'd otherwise be, and near 50% higher in the densest pocket.
See how → 06 · Policy evaluationFound that two-thirds of listings sit just outside the by-law's reach, and those are linked to 2.5× the rent impact.
See how → 07 · Synthesis & frameworksProposed a new framework for why very different cities face the same affordability squeeze: global platforms reach into local markets.
See how → 08 · CommunicationTurned 135 pages of analysis into a 25-minute talk and plain-language summaries, recognized with a methodology award.
See how →Independently led the short-term-rental stream of a university research project: gathered and prepared large administrative and third-party datasets, ran the statistical analysis, and wrote briefing notes on how short-term rentals may raise local poverty rates.
Integrated 20 years of census data (2001–2021) into a single spatial analysis. Drew rings at increasing distances around key sites (GIS buffers) to reveal building-age patterns and how neighbourhood change affects vulnerable residents.
Led a field team studying rural poverty: designed the interview guide, conducted semi-structured interviews with community members, and co-authored a paper published in a regional journal (citation on request).
Lead the short-term-rental sub-project end to end: data collection and preparation, statistical analysis in Stata, spatial visualization in QGIS/ArcGIS/Tableau, and plain-language briefing notes for cross-functional teams.
Designed and delivered a full quantitative study of Airbnb's impact on Toronto rents: original 10-source dataset, custom accessibility measures, econometric modeling with bias correction, and a policy evaluation that uncovered a regulatory loophole.
Cleaned and integrated 20 years of census data for spatial analysis; measured how socio-economic conditions shift with distance from key sites (GIS ring buffers); validated and presented findings.
Explained complex concepts to students, gave constructive feedback, and managed high-volume grading under strict deadlines.
Coordinate daily with three international companies, ensuring accurate translation and knowledge transfer across concurrent projects.
Two years of statistics at a community nonprofit serving people living with mental illness: researched the social and psychological factors affecting clients, handled sensitive personal information under strict confidentiality protocols, led community engagement and outreach, and ran budget analysis to support program planning.
Led qualitative fieldwork on rural poverty (published in a regional journal), supported survey research on education equity, and provided in-residence support for people with developmental disabilities.
Thesis: quantitative analysis of Airbnb's impact on long-term rents in Toronto. Independent reading course in applied housing economics.
Graduated as valedictorian.
Python Programming (University of Helsinki, online) · Social Surveys Practices (Peking University Summer School).
Awarded for the thesis research presented on this site:
"Original dataset creation, complex data analysis methods, and clear communication of findings."
I'm based in Kitchener, Ontario, and open to planning, housing, and data analyst roles. If your team needs someone who can take a question from raw data to a clear recommendation, I'd love to hear from you.