This methodology accompanies Redfin’s report about how letting home sellers test the market before officially listing could boost housing supply.
Every year, many homeowners are discouraged from selling by the high costs and uncertainty associated with listing and selling a home. These effective costs include both direct expenses and other factors that effectively reduce the seller’s net outcome.
- Direct financial costs: repairs, staging, real estate agent commissions, taxes
- Time and effort costs: the time required to prepare the home, coordinate with agents and buyers, accommodate showings
- Market outcome costs: the risk of receiving a lower-than-expected sale price, extended time on market
- Privacy and disruption costs: loss of privacy from showings and market exposure, and loss of full use and enjoyment of the home
This analysis focuses on two ways phased marketing–specifically, the ability to test pricing and collect feedback from buyers before listing on an MLS, or control market exposure–can reduce some of these effective costs:
- Improved pricing accuracy: Sellers can test pricing and gather feedback before going public, reducing the risk of mispricing and the associated costs of extended time on market and/or stigma from price drops
- Improved privacy and convenience: Sellers can limit public exposure and maintain control over the pace and quantity of tours with less time pressure
We estimate that these benefits are worth 1.2–2.4% of their home’s value to sellers. We created a model to estimate how sellers would respond to these benefits, and estimate that phased marketing could increase annual listings by 6–12% in markets where it is widely available.
Background
As Figure 1 illustrates, sellers and their agents under the traditional marketing model must decide on a list price prior to listing the home based on limited information such as “comps,” or recent sales of comparable homes. Upon choosing a price and listing the home, days on market and all price changes are public information moving forward.
Sellers and their agents typically decide on a pricing strategy based on how the seller values time, sale price, and convenience. Some sellers may prefer a quick sale, and aim to price cautiously to avoid the risk of extended time on market. Other sellers may prefer to maximize their sale price, and aim to price optimistically even with the likelihood of longer time on market.
But only with the benefit of hindsight do sellers learn how accurately they have priced for their strategy. The first few weeks on the market provide crucial feedback to sellers. Underpriced sellers enjoy elevated buyer interest and receive offers faster than expected but are left wondering if a higher list price would have yielded a better sale price.* Overpriced sellers experience limited buyer interest, indicating that they will experience longer-than-expected time on market and risk of a lower-than-expected sale price.** Consequently, many prospective sellers are potentially dissuaded from listing due to the costs and uncertainty they face about price and time on market.***
How significant is the risk of mispricing? One way to measure pricing accuracy is to compare the initial list price of a home to its eventual sale price. A home that was initially priced much higher than its eventual sale price was likely overpriced; a home initially priced much lower than its eventual sale price was likely underpriced.
Figure 2 shows the share of sellers (bars) and average expected time on market (line) by their initial pricing accuracy. We estimate the relationship between pricing accuracy and time on market using a regression that controls for the property’s predicted value and the local market’s typical pricing patterns to reflect “true” mispricing.****
Figure 2 illustrates that mispricing is a common phenomenon with significant impact on seller outcomes.
- A significant share of sellers misprices their home. One-third of sellers underprice or overprice, with extreme overpricing (overpriced more than 10%) more common than extreme underpricing (underpriced more than 10%).
- Mispricing dramatically affects time on market. The blue line shows the average predicted months on market by pricing accuracy, ranging from less than 1 month for the most underpriced to over 4 months for the most overpriced. Underpriced sellers sell in approximately 1 month or less. Accurately priced sellers sell within 1.5-2.5 months. Sellers overpriced by 5-10% spend 3-4 months on market.
Estimation of Phased Marketing Value and Effect on Listings
We model phased marketing as benefiting sellers through 1) improved pricing accuracy and 2) improved privacy and convenience. We assume that pricing accuracy is improved by allowing sellers to test prices and collect feedback from buyers without a public price or time on market history, and privacy and convenience is improved for sellers who prefer discretion or want to limit the number or pace of tours.
We calculate the following values to estimate the value and inventory impact of these benefits:
- The share of homeowners who stand most to benefit from phased marketing through improved pricing accuracy or privacy and convenience
- The average benefit as a percentage of home value for these sellers, which is equivalent to a corresponding reduction in the cost of selling
- The percentage increase in annual listings given those cost reductions
- The cumulative inventory effect of these new listings, as they unlock additional listings and sales by coming on market
Share of homeowners who stand to benefit most: As shown in Figure 2, nearly 40% of sellers misprice by at least 5%, with roughly equal shares over- and underpricing. We conservatively assume that only half of these mispriced sellers, or 20% of all sellers, would have benefited from the additional pricing information that phased marketing can provide. In addition, we assume 5% of all households have high privacy and convenience concerns that would make phased marketing beneficial.*****
Average benefit for sellers who stand to benefit most: We measure the benefits of improved pricing accuracy in terms of a reduced likelihood of extended time on market and a reduced likelihood of price drops. The cost of time on market is primarily the cost of time to the seller, while the cost of price drops is the stigma associated with a price drop history.† Because time on market and sale price are interrelated, we treat these benefits as mutually exclusive to avoid double counting and assume that sellers who avoid mispricing benefit equally from each. Note that while we frame these benefits in terms of how overpriced sellers could benefit, the benefits equally reflect the value of avoiding underpricing as well.††
The table below summarizes the benefit calculations:
| Benefit type | Potential share of homeowners benefiting | Benefit (% of home value) | Benefit details | Sources of benefit value calculation |
| Reduced likelihood of extended days on market (DOM) | 10% | 1-2% | Assumption: Phased marketing reduces DOM by 2 weeks
Value: 2-4% of home value per month |
Merlo, Ortalo-Magne, and Rust (2015) – 4% per month
Anenberg, Elliot (2016) – 1% per month Genesove and Mayer (1997) – 1.6% per month††† |
| Reduced likelihood of price drops | 10% | 1-2.5% | Assumption: Phased marketing reduces on-MLS price drop risk by 50%††††
Value: 2-5% of home value |
Redfin analysis of MLS data – 5%†††††
Knight (2002) – 1.8-3.4% Gordon and Winkler (2017) – 6-9%‡ |
| Improved privacy and convenience | 5% | 2-3% | Assumption: Phased marketing period avoids public listing, open houses, unqualified showings; seller retains option to go public
Value: 2-3% of home value |
Assumed value, based on indirect evidence from iBuying‡‡ |
| Weighted average benefit | – | 1.2% (low) / 2.4% (high) | – | – |
| As % of baseline cost (8% of home value) | – | 15% (low) / 30% (high) | – | – |
The calculation above shows that the average benefit is equivalent to 1.2-2.4% of home value for those sellers who stand most to benefit from phased marketing. Given a baseline cost of 8% of home value, this is equivalent to a 15-30% reduction in the cost of selling.‡‡‡
Percentage increase in annual listings driven by phased marketing: We then estimate how many more listings would result given sufficient access to phased marketing. Based on published estimates of the cost elasticity of listings, or the percentage change in listings expected given a 1% change in the cost of selling, we assume an elasticity of 1.‡‡‡‡ This implies a 15-30% increase in the share of affected sellers listing each year. With 25% of homeowners standing to benefit most, the aggregate listing response is therefore 3.75-7.5%.
Cumulative inventory effect of new listings from phased marketing: Finally, we multiply the aggregate listing response by a multiplier of 1.6 to reflect the expected downstream effect of additional listings, for a combined inventory increase of 6-12%.‡‡‡‡‡ The multiplier reflects the downstream effect of additional listings becoming unlocked through a sell-then-buy chain between buyers and sellers. Based on 2025 NAR data, we assume 75% of buyers in the near-term will be repeat buyers.§ Additionally, we estimate that at least 50% of repeat buyers plan to sell their current home before buying.§§ Consequently, 37.5% (or 75% x 50%) of these new listings unlock an additional home once sold, leading to an aggregate multiplier effect of 1.6.§§§
With a baseline annual listings of 4.6 million for 2026, this would imply a direct increase in listings by 172k-344k, and a total increase of 276k-552k.§§§§
Endnotes
*A low list price could, for example, signal to buyers that there is a defect in the home or that the seller is highly motivated, reducing their bargaining power. Although deliberate underpricing has the potential to spark a bidding war and yield a higher sale price (with “auction fever” pushing buyer valuations up), there are diminishing returns to lowering the price. A low list price anchors bidders to a low valuation and discourages high-value buyers from even viewing the home, offsetting the benefits of auction fever. This is consistent with empirical evidence, such as Hammervold et al. (2025) who find that strategic underpricing increases bidding “temperature” but fails to raise the sale price.
**A high list price can anchor buyers to a high valuation and signal that the seller is patient, improving their bargaining power. However, even moderate overpricing carries the risk of the home developing stigma through excessive days on market and price drop history that can ultimately reduce the final sale price. Benjamin and Chinloy (2000) also find that overpricing yields minimal sale price benefits.
***A 2025 survey found that the top four reasons sellers indicated as “scaring” them from potentially selling their home were the stress of selling, the cost of selling, not being able to afford a new home, and not selling for enough. (Pisano, 2025) A 2020 Zillow survey found that sellers who have reported being likely to move within the next three years report waiting for a more favorable price, concerns about other home prices, and life uncertainty as their primary reasons for not moving. (Garcia, 2024) Bottan and Perez-Truglia (2025) also find that a 1 percentage point increase in expected future home price appreciation reduces the propensity to sell by over 2.5 percentage points for sellers already on the market.
****For example, a seller whose original list price is 10% above their final sale price, in a market where similar sellers tend to list 5% above, would be considered here to be overpriced by 5%. We apply this adjustment to capture the effect of mispricing relative to other sellers. To do so, we regress the log ratio of the original list price and final sale price on time on market, with controls for the log predicted sale price, location, and time of the sale. In Figure 2, the measure of original list price / sale price reflects the residualized value.
*****Survey evidence shows that privacy and convenience are significant concerns for sellers. (Bulloch , 2022) The shares of off-market sales and sales to iBuyers also provide suggestive evidence of the high share of homeowners who value privacy and convenience. Redfin has previously estimated that at least 2% of sales occur off-market, and iBuyers have been estimated to account for an additional 1-3% of sales nationally. (NAR Survey, 2026).
†The amount of the price drop is not itself a cost to the seller, as the seller was not necessarily going to sell at the initial list price amount. Rather, the cost of a price drop is the stigma associated with the action, as buyers view the price drop as a signal of impatience or low market value of the home. For example, a seller who cuts their price from $500k to $450k may be more likely to sell for less than $450k than an identical seller who initially priced at $450k (without a price drop history).
††Specifically, we assume the benefits are symmetric for underpricing. For the DOM benefit, reduced DOM without a reduction in sale price offers the same value as the corresponding increase in sale price without a change in DOM. For the price drop benefit, the value can be interpreted as an increase in sale price that comes from being less likely to underprice.
†††This estimate is for low-equity homeowners who may be particularly willing to trade time for a higher sale price due to loss aversion.
††††Using MLS data on single family home sales between 2023-2025 we estimate that a seller who reduces their degree of overpricing from 110% to 105% reduces their risk of a price drop by approximately 50%. Hayunga (2026), in a recent working paper, estimates that marketing off-MLS effectively reduces price drop risk by 80%. A 2024 analysis by Compass found that pre-marketed homes were about 30% less likely to have a price drop.
†††††We regress the log sale price on an indicator for whether the home had a price drop, with controls for the property’s predicted value, property characteristics, market-level sale metrics (median DOM, sale price growth, and average sale to list), and region and period fixed effects. With this specification the coefficient on the price drop indicator is 5.4%. This result is robust to additional controls, such as number of price drops, total DOM, time from list to last price drop, and original list price. Knight (2002) conducts similar tests and finds a range of estimates from 1.8-3.4%.
‡Gordon and Winkler (2017) estimate the percent reduction in sale price is 2 to 3 times as large as the reduction in list price. We estimate the median list price change is approximately 3%, which implies a 6-9% reduction in sale price due to stigma.
‡‡Seiler and Yang (2023) estimate that homeowners sell to iBuyers at around 5% less in exchange for convenience and find evidence that more impatient sellers and sellers who have previously attempted to sell on the MLS and failed are more likely to sell to iBuyers.
‡‡‡We choose 8% to capture standard seller-borne commissions and closing costs of 3-4%, plus 2-4% in repairs and additional time and hassle costs. Haurin and Gill (2002) estimate it as 3% of sale price plus 4% of household income.
‡‡‡‡Published estimates consistently find large values for this elasticity. For simplicity of interpretation, given the range of our value estimates, we assume an elasticity of 1. For reference, Van Ommeren and Van Leuvensteijn (2005) find that a 1 p.p. increase in a sales tax (that starts at 6%) leads to an 8% reduction in moving rates, implying an elasticity near 1 assuming total baseline costs of 10-12% (because the sales tax in their context was 6%, total selling costs were likely higher in their context as well). Similarly, Han, Ngai, and Sheedy (2025) find that a 1.3 p.p. increase in a transfer tax led to 12% lower moving hazard rate, which also suggests an elasticity near 1 assuming 8% in seller costs.
‡‡‡‡‡Our approach to calculating the multiplier effect is inspired by Anenberg and Ringo (2022), who show that the multiplier depends on the share of buy-first and sell-first households. They estimate a two-year multiplier between 1.48 to 2.8, though their measure is intended to reflect the total change in sales based on the entry of first-time buyers. Other evidence comes from the literature on vacancy chains, or the number of moves caused by the creation of new housing supply. In a recent working paper, French and Gilbert (2024) find each new home creates 0.9 additional moves downstream, or a multiplier of 1.9; similarly, Bratu et al. (2023) find that each new centrally-located market-rate home creates 0.66 homes in bottom-half income zip codes, implying a multiplier well above 1.66.
§ NAR Buyer Profile (2026).
§§This estimate is a conservative lower-bound based on a Redfin analysis. A higher sell-first share implies a greater multiplier effect. Survey evidence suggests the sell-first share may vary between 59% (Berchick and Garcia, 2025) and 65% (Xu, 2023).
§§§The multiplier is 1 / (1 – 37.5%) = 1.6.
§§§§This value represents a 2% increase from 2025 inventory, consistent with Redfin’s existing home sales forecast of 2% year-over-year growth in 2026.
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