To Own or to Rent? The Effects of Transaction Taxes on Housing Markets

Layer 1 — Summary

Using sales and leasing transaction records for the Greater Toronto Area (2006–2018), this paper finds three novel effects of a higher property transaction tax: higher buy-to-rent transactions alongside lower buy-to-own transactions despite both being taxed, a lower sales-to-leases ratio, and a lower price-to-rent ratio. The empirical identification exploits the City of Toronto’s introduction of a city-level Land Transfer Tax (LTT) in February 2008 — covering only the city and not surrounding GTA municipalities — comparing outcomes on opposite sides of the city border before and after the tax change. A 1.3 percentage-point higher effective LTT rate causes buy-to-rent purchases to rise by 9.3% while owner-occupier purchases fall by 9.6%; the leases-to-sales ratio rises by 26% and the price-to-rent ratio falls by 3.8%. To explain these facts, the paper develops a search model featuring household tenure choice (own vs. rent) subject to heterogeneous credit costs, endogenous homeowner moving decisions, and free entry of buy-to-rent investors; the key mechanism is that the LTT reduces homeowners’ mobility — because owner-occupiers expect to transact multiple times over their lifetimes and thus bear the tax repeatedly — discouraging entry into ownership and raising demand for rentals, which in turn attracts investor entry even though investors too pay the tax, since investors need not re-transact whenever a tenant vacates. The implied deadweight loss is large at 111% of tax revenue, with more than half of this due to distorting decisions to own or rent; taking the rental market into account accounts for losses equal to 73% of tax revenue, which is two-thirds of the total loss.

Layer 2 — Q&A

Q: What are the three novel empirical facts documented in this paper?

A: Using MLS data on both sales and leases in the Greater Toronto Area, the paper documents: (1) a 1.3 pp higher effective LTT rate causes buy-to-rent (BTR) investor purchases to increase by 9.3%, in stark contrast to a 9.6% fall in owner-occupier (buy-to-own) purchases — a divergence that is counterintuitive because both types of buyer are subject to the same tax; (2) the ratio of leases to sales rises by 26%, indicating that rental-market activity increases relative to ownership-market activity; and (3) the price-to-rent ratio falls by 3.8%, meaning house prices decline relative to rents.

Q: What is the empirical identification strategy and why is it credible?

A: The paper uses a geographic regression discontinuity approach comparing communities on opposite sides of the Toronto city border, where the new city-level LTT applies on one side but not the other, in a difference-in-differences framework spanning January 2006–January 2008 (pre-policy) and February 2008–February 2012 (post-policy). The sample is restricted to properties within 3 or 5 km of the boundary. The paper verifies that property characteristics do not differ significantly across the border and that cross-border differences do not change after the LTT, supporting the parallel-trends assumption. The effective LTT rate increase is measured at 1.3 percentage points (assuming 40% first-time buyers, who receive a partial exemption). Buy-to-rent transactions are identified in the MLS data by matching properties that appear in both the sales and leases datasets within an 18-month window following sale.

Q: What is the intuition for why the LTT raises buy-to-rent investment even though it taxes investors?

A: The mechanism hinges on the asymmetry in expected future transaction costs between owner-occupiers and investors. Owner-occupiers face idiosyncratic match-quality shocks — they periodically want to move to a different property as their circumstances or preferences change — so choosing homeownership means expecting to pay the LTT on each future move. This makes homeownership less attractive relative to renting, reducing household entry into the ownership market and increasing demand for rental properties. Investors (landlords), by contrast, do not need to re-transact in the ownership market simply because a tenant moves out; they retain the property and find a new tenant. Investors therefore face a lower expected frequency of LTT payments per year of property holding than owner-occupiers. As a result, the LTT’s negative effect on investor returns is smaller in magnitude than the increase in rental demand it generates. In equilibrium, the price-to-rent ratio falls by enough to attract more BTR investors in spite of the direct cost the tax imposes on them, and investor purchases rise.

Q: How does the LTT affect homeowner mobility (the “lock-in” effect) and what are its welfare implications within the ownership market?

A: The LTT makes existing homeowners more tolerant of poor match quality with their current property, since the cost of moving — paying the tax again — has risen. Moving rates therefore decline as households remain in properties for longer on average. To mitigate future tax costs, buyers also become more selective (“picky”) when initially matching with a property, requiring higher match quality before purchasing. This reduces the frequency of moves but increases the cost and duration of search for new buyers. The welfare consequences within the ownership market are: (a) misallocation of properties among owner-occupiers as average match quality falls because households move less often to renew it; partially offset by (b) higher initial match quality for newly matched buyers, but at the cost of longer search. The LTT-induced distortions within the ownership market account for a loss equal to 38% of tax revenue.

Q: What are the model’s quantitative predictions for the four-year post-reform period, and how do they compare to the empirical estimates?

A: The model is calibrated to the City of Toronto for 2006–8 (homeownership rate ~54%) and simulated for a 1.3 pp LTT increase, with the mobility hazard rate used as the internal calibration target. For the four-year period following the tax change, the model predicts: owner-occupier transactions fall by 14%; buy-to-rent transactions rise by 35%; the leases-to-sales ratio rises by 15%; the price-to-rent ratio falls by 1.6%; and the homeownership rate falls by 0.23 percentage points. These figures are broadly consistent in magnitude with the estimated LTT effects on the variables not directly targeted in calibration (i.e., the transaction-volume and price-to-rent results from the empirical estimation).

Q: What are the long-run (steady-state) effects and why do they differ from the four-year effects?

A: Tenure-choice variables are very slow to adjust because annual flows are small relative to housing stocks. In the new steady state, the homeownership rate falls by 2.4 percentage points and the leases-to-sales ratio rises by 23% — both substantially larger than the four-year effects. By contrast, four-year effects on owner-occupier transactions and the price-to-rent ratio are already close to their new steady states. Buy-to-rent transactions overshoot their steady-state level (the four-year rise of 35% compares to a steady-state rise of 5.1%) because of a one-off surge in investor entry as the rental market absorbs the transition; once the stock of rental properties has adjusted, the flow of new buy-to-rent purchases settles lower.

Q: How are the welfare (deadweight) losses decomposed across distortion channels?

A: The new LTT generates a total welfare loss equivalent to 111% of the extra revenue it raises. The decomposition is: distortions to flows between the rental and ownership markets (i.e., the tenure-choice margin) account for a loss equal to 60% of extra revenue; distortions within the rental market account for 13% of tax revenue; distortions within the ownership market (lock-in and match-quality misallocation) account for 38% of tax revenue. The presence of the rental market in the analysis — encompassing both the across-market and within-rental-market channels — accounts for a loss equivalent to 73% of tax revenue, which is two-thirds of the total loss. The paper characterises this as “large.”

Q: What is the across-market misallocation mechanism behind the 60% welfare loss from tenure distortions?

A: Because owner-occupiers expect to transact more frequently than buy-to-rent investors, the same ad valorem tax falls more heavily on owner-occupiers. In equilibrium, the cost of credit paid by the marginal home-buyer must fall — that is, fewer creditworthy households enter ownership. This displaces some creditworthy households into the rental market, creating a misallocation: properties are allocated away from owner-occupiers (who value them as a place of residence and benefit from match quality) toward rentals intermediated through investors. The welfare loss arises because credit-worthy households who would prefer to own are now renters, and the resource costs of intermediating through investors are incurred unnecessarily.

Q: What policy experiment does the paper consider beyond the baseline LTT analysis?

A: The paper studies an alternative tax structure that imposes a higher LTT rate on buy-to-rent investors relative to owner-occupiers, calibrated to nullify the implicit tax advantage investors enjoy under a uniform rate. By raising barriers to investor entry, this differential tax reduces the across-market welfare losses from lower homeownership. However, the paper notes an important caveat: pushing the investor tax rate ever higher to boost homeownership would ultimately produce large welfare costs in the opposite direction, as households who cannot qualify for mortgage credit (uncreditworthy households) would be displaced into the ownership market by a shortage of rental properties. Investors play a socially valuable role in providing housing access to households who cannot or choose not to bear the costs of credit.

Q: What data source is used and why is it unusually well-suited to this analysis?

A: The paper uses Multiple Listing Service (MLS) records from the Toronto Regional Real Estate Board covering the Greater Toronto Area, 2006–2018. The dataset is distinctive in including both sales transactions and lease transactions, allowing the paper to match the two and construct the novel buy-to-rent identifier. MLS data cover approximately 78% of detached-house transactions in the Toronto Land Registry for 2006–2012, and the rental listings capture over 90% of properties listed on alternative platforms. This combination of sales and lease records is what makes it possible to document the three novel empirical facts and to study both the ownership and rental markets jointly.

Key Concepts

Buy-to-rent (BTR) transaction: In this paper’s definition, a sale in the ownership market where the buyer subsequently lists the same property on the rental market within 18 months. BTR buyers are investors/landlords who supply rental housing by purchasing from the ownership market. Distinct from buy-to-own (owner-occupier purchases) and buy-to-sell (flipping) transactions. Identified in the MLS data by matching address and transaction dates across the sales and leases databases.

Buy-to-own (BTO) transaction: A sale in the ownership market where the buyer occupies the property as a homeowner — the residual category after removing BTR and buy-to-sell transactions from total sales. In the City of Toronto, the fraction of all transactions classified as BTO declined from 89% to 84% between 2006 and 2017.

Effective LTT rate: The mean land transfer tax paid as a percentage of the sales price, combining provincial- and city-level taxes, averaged over detached-house transactions in the City of Toronto and adjusted for first-time buyer exemptions. The introduction of the city-level LTT in February 2008 raised the effective LTT rate by 1.3 percentage points (assuming 40% first-time buyers).

Match quality: In the paper’s search model, the idiosyncratic value a particular household places on a particular property, which evolves stochastically over time. When match quality deteriorates sufficiently, a homeowner wishes to move to a better-matched property. Match quality is the source of the “lock-in” effect: higher transaction taxes raise the threshold quality decline a household is willing to tolerate before moving, reducing mobility. Because investors are not tied to a specific property in the same way (a tenant moving out does not require the investor to transact), this mechanism falls more heavily on owner-occupiers than on BTR investors.

Lock-in effect: The reduction in homeowner mobility caused by a higher transaction tax. Homeowners become more tolerant of deteriorating match quality (stay longer in poorly matched properties) and more selective when initially purchasing (require higher match quality to justify the transaction cost). The paper treats this as operating on the intensive margin of homeownership decisions, contrasted with the extensive margin (the own-vs.-rent choice).

Credit cost / credit friction: Heterogeneous household-level costs of accessing mortgage finance or credit. In the model, a household must pay a credit cost to enter the ownership market. Households with lower credit costs are more likely to choose homeownership; a higher transaction tax effectively raises the total cost of ownership (since it must be paid on each future move), shifting the margin at which the credit cost equals the net benefit of owning, thereby reducing the equilibrium homeownership rate.

Leases-to-sales ratio: The ratio of new lease transactions to sales transactions in the housing market, used as a measure of the relative activity of the rental and ownership markets. A higher ratio indicates more households are being accommodated in the rental market relative to the ownership market. The LTT raises this ratio by 26% in the empirical estimation and 15% in the four-year model simulation, with a steady-state increase of 23%.

Price-to-rent ratio: The ratio of house prices to rents, used as a summary statistic for the relative cost of owning versus renting. In the paper’s model, a fall in the price-to-rent ratio is the price signal that attracts additional buy-to-rent investor entry: as tenure-choice distortions shift more households toward renting, rents rise relative to prices, improving the return to BTR investment until the rental market clears. The LTT lowers the price-to-rent ratio by 3.8% empirically and 1.6% in the four-year model simulation.

Deadweight loss as a fraction of tax revenue: The welfare cost of the LTT measured in units of tax revenue raised, allowing comparison across tax instruments. The paper finds a deadweight loss of 111% of tax revenue for the Toronto LTT. Prior literature, which focused only on the intensive margin (mobility distortions within the ownership market), missed the across-market and within-rental-market channels that together account for 73 percentage points of this total.

Summary based on published open-access version. AI-assisted, human review pending.