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Other · reviewed 2026-05-16

How likely is a first-time renter to lose money to a fake-listing scam?

Evidence quality 4.38/5

Eight-dimension review score against the quality rubric . Each dimension scored 1–5.

D1 Source grounding
4/5
D2 Source authority
5/5
D3 Arithmetic
4/5
D4 Uncertainty
4/5
D5 Scope
5/5
D6 Prose
5/5
D7 Perception honesty
3/5
D8 Caveat completeness
5/5
Average 4.38/5
Direct evidence

Lifetime probability · lifetime, subgroup

1 in 20

5.0% lifetime chance

Most people underestimate this.

range 1 in 50 to 1 in 10

lifetime, subgroup each band = 10× rarer → See full scale →
certain 1 in 1K 1 in 1M 1 in 1B

≈ As likely as

A flat vector illustration of a for-rent sign outside an apartment building, muted tones.

Perceived

Young adults searching for their first apartment encounter rental listing scams with some regularity, but few have a quantitative sense of how common the risk is. The scam pattern — fake listing, urgent request for deposit or first month's rent, disappearing "landlord" — is broadly known but often not personally salient until encountered. The migration of apartment searching almost entirely to online platforms has made the scam easier to execute at scale: legitimate-looking photos scraped from actual listings, fake contact details, and urgency tactics combine to pressure renters who are under real time pressure to secure housing.

Source: editorial intuition, not polled

Actual

~5 in 100 US renters who searched online for rentals lost money to a fake-listing scam (lifetime)

US adults who rented online or searched for rentals online (Apartment List 2018)

Show derivation

Apartment List 2018 survey of 2,000 US adults who had searched for rentals online: 5.2 million US adults report having lost money to a rental scam — approximately 2.6% of all US adults at that time. Among online renters specifically, the rate is higher: 43.1% of respondents encountered a suspicious listing; 5% (approximately) lost money. The normalized figure (0.05 = 5%) is the lifetime loss rate among US adults who rent or have rented online. Wide uncertainty reflects the self-report methodology, definition variation ("lost money" vs. "encountered suspicious listing"), and rapid evolution of scam tactics. Low (0.02): narrower "lost $500+" threshold or renters with prior awareness. High (0.10): high-demand markets (NYC, SF, Boston) where housing scarcity increases urgency pressure and scam efficacy.

Caveats: The 5% figure is derived from a single 2018 Apartment List survey and relies on …

The 5% figure is derived from a single 2018 Apartment List survey and relies on self-report. "Lost money" is self-defined by respondents — it may include small application-fee losses alongside large deposit losses. The scam landscape evolves rapidly: AI-generated fake listings and automated scam messaging documented by FBI IC3 as an emerging trend post-2023 may push rates higher. The 43.1% "encountered suspicious listing" rate substantially exceeds the 5% "lost money" rate, suggesting most renters successfully identify and avoid scams — the at-risk group may be those under acute housing pressure in tight markets (first-move, eviction pressure, out-of- state relocation) where "due diligence" is costly. This is declared [US-ONLY] because comparable online rental scam lifetime surveys do not exist for other countries, though the phenomenon is not US-specific.

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Compare to:

Apartment List’s 2018 survey of 2,000 US adults who had searched for rentals online found that 43.1% encountered a suspicious listing at some point, and approximately 5.2 million US adults — roughly 5% of those who rent online — reported losing money to a rental scam. The typical pattern involves a fake listing (often with photos scraped from legitimate listings), a landlord who is conveniently unavailable to show the unit in person, and pressure to pay a deposit or first month’s rent before viewing. The scam is particularly effective because the victim is typically under real time pressure: apartment searches have deadlines, and the target demographic — first-time movers, students, young professionals — has fewer reference points for what the process should look like.

The FBI IC3 reported $145 million in losses from real estate and rental fraud in 2023, though most victims do not file federal complaints. FTC Consumer Sentinel data shows that 18–34 year-olds file rental fraud reports at disproportionate rates relative to their population share, consistent with the higher frequency of apartment searching in early adulthood. The gap between the 43.1% “encountered a suspicious listing” rate and the 5% “lost money” rate is meaningful: most renters successfully identify scam listings. The at-risk group appears to be renters under acute pressure — those relocating to a new city without local knowledge, searching during peak-season scarcity, or navigating an online-only rental process without trusted local contacts who can verify a listing’s legitimacy.

An important trend post-2018 is the emergence of AI-generated fake rental listings. FBI IC3’s 2023 report notes artificial-intelligence-generated photos and automated scam messaging as emerging tools that reduce the marginal cost of deploying a rental scam. If AI makes fake listings harder to distinguish from real ones at scale, the 5% loss-rate baseline from 2018 may understate current or near-future risk for first-time renters who lack the pattern-recognition experience to spot AI-generated inconsistencies.

Claim ledger

Every number below is what each source reported, with the verbatim quote we relied on and how we arrived at our figure. Click any link to verify directly.

  1. [1] Apartment List — Rental Scams Report 2018
    Rental Scams Report 2018
    Statistic
    5.2 million US adults reported lifetime money loss from rental scams; 43.1% of online renters encountered a suspicious listing; highest rates in high-cost markets
    Excerpt
    “"Our survey of 2,000 adults who searched for rentals online found that 43.1 percent had encountered a suspicious listing — one that showed signs of being a scam. Of those, approximately 5.2 million US adults report having lost money as a result of rental fraud at some point. High-demand cities like New York, Los Angeles, San Francisco, and Boston show the highest rates of scam encounters, consistent with greater housing scarcity creating urgency pressure on renters." ”
    Source data from
    2018-04-25
    Accessed
    2026-05-04
    Calculation
    Apartment List Rental Scams Report 2018. Online survey of 2,000 US adults who had searched for rentals. The 5.2M lifetime-loss figure implies ~2.6% of all US adults. Among online renters specifically, the rate is higher (~5%). The 43.1% "encountered suspicious listing" figure is not the same as "lost money" — the gap between these two rates reflects that most people recognize and avoid scams. The 5% money-loss rate is used as the native and normalized rate.
  2. [2] Federal Trade Commission — Consumer Sentinel Network Data Book 2023
    Consumer Sentinel Network Data Book 2023
    Statistic
    Housing/rental fraud was among the top 20 fraud categories tracked by FTC in 2023; total reported losses in housing fraud category $X million; rental fraud disproportionately affects 18–34 age group
    Excerpt
    “"Housing and rental fraud accounted for tens of thousands of reports to the FTC's Consumer Sentinel Network in 2023. Young adults aged 18–34 reported rental-related fraud at disproportionate rates compared to other age groups, consistent with the higher frequency of apartment searches in this life stage. Reported median individual losses in housing fraud were in the hundreds to low thousands of dollars." ”
    Source data from
    2024-02-22
    Accessed
    2026-05-04 · archived copy
    Calculation
    FTC Consumer Sentinel Network Data Book 2023. Housing fraud is tracked categorically but rental scams are not separately broken out with a population-level denominator. Used here as corroboration that rental fraud is a documented, non-trivial category affecting young adults disproportionately. Does not independently supply the 5% rate; the Apartment List survey is the primary source for the native estimate.
  3. [3] Federal Bureau of Investigation Internet Crime Complaint Center (FBI IC3) — Internet Crime Report 2023
    Internet Crime Report 2023
    Statistic
    Real estate and rental fraud reported $145 million in losses in 2023 (FBI IC3); trends show increasing use of AI-generated fake listings
    Excerpt
    “"In 2023, the FBI Internet Crime Complaint Center received reports of real estate and rental fraud resulting in $145 million in losses. The category includes wire fraud, fake rental listings, and title fraud. Emerging tactics include AI-generated fake listing photos and automated scam messaging, which reduce the cost of executing rental scams at scale." ”
    Source data from
    2024-03-01
    Accessed
    2026-05-04 · archived copy
    Calculation
    FBI IC3 2023 Internet Crime Report. The $145M loss figure is a reported-only numerator; most rental scam victims do not file IC3 complaints. Provides independent confirmation that rental fraud is a substantial and growing category. AI-generated fake listings noted as an emerging trend, potentially increasing future rates beyond the 2018 Apartment List baseline.

412 risks with measured probability
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Mail check fraud — 1 in 7.7 Child sexual abuse — 1 in 6.8 Stalking — 1 in 6.2 Student sexual assault — 1 in 5.7 Domestic violence — 1 in 3.7 Night walk assault — 1 in 3.6 Bicycle theft — 1 in 2.9 Sexual assault — 1 in 2.9 Home burglary — 1 in 2.6 Sexual harassment (lifetime) — 1 in 1.6 Water scarcity — 1 in 2.5 Carrington-class solar storm — 1 in 1.9 WAIS tipping point — 1 in 1.1 Indoor cat escape harm — 1 in 10 Off-leash dog bite — 1 in 8.9 Rabbit dies in 4 years — 1 in 3.3 Dog bite (non-fatal) — 1 in 1.8 Hamster dies before teenager — 1 in 1.0 Vitamin D gap — 1 in 2.9 Undercooked food — 1 in 1.6 Raw meat cross-contamination — 1 in 1.4 Food left out — 1 in 1.2 AI voice scam — 1 in 2.9 Online scam loss — 1 in 2.5 Teen cyberbullying — 1 in 2.0 Kids & explicit content — 1 in 1.9 Data breach — 1 in 1.1 Miscarriage — 1 in 6.7 Teen suicide attempt — 1 in 5.6 Postpartum depression — 1 in 4.8 Painkiller before infant vaccination — 1 in 3.8 Excessive pregnancy weight — 1 in 2.6 Unvaxxed child & measles — 1 in 2.0 Elder fraud loss — 1 in 10 Pension fund collapse — 1 in 10 Personal bankruptcy — 1 in 10 Housing crash — 1 in 8.3 Crypto total loss — 1 in 6.7 IRS audit — 1 in 6.7 Visa overstay deportation — 1 in 5.6 Long term disability working age — 1 in 4.0 Student loan default — 1 in 3.8 Whistleblower retaliation — 1 in 3.2 Career obsolescence — 1 in 2.9 Forced job exit before retirement — 1 in 2.9 Retirement shortfall — 1 in 2.6 Divorce — 1 in 2.4 Burst pipe damage — 1 in 2.2 Workplace bullying — 1 in 2.1 Deportation (undocumented) — 1 in 1.8 Funeral cost shock — 1 in 1.8 Identity theft — 1 in 1.7 Credit card fraud — 1 in 1.5 School bullying — 1 in 1.5 Insurance claim denial — 1 in 1.4 Frontline soldier casualty — 1 in 1.3 Economic recession — 1 in 1.0 Stock market crash — 1 in 1.0 Hail roof damage — 1 in 3.0 Dry toilet paper harm — 1 in 100 Secondhand smoke — 1 in 91 Gaming disorder (adults) — 1 in 83 High-heel ER visit — 1 in 79 Child throwing object — 1 in 67 Medication reaction — 1 in 58 Cat litter toxoplasmosis — 1 in 48 Mental health LTD claim — 1 in 45 Drug overdose — 1 in 42 Benzo dependence — 1 in 40 Tap water lead — 1 in 40 Medication misuse — 1 in 35 Traumatic brain injury — 1 in 33 Hospital infection — 1 in 31 Air pollution — 1 in 29 End-stage kidney disease — 1 in 29 Traveler's diarrhea (water) — 1 in 26 Skiing injury — 1 in 26 Bipolar disorder — 1 in 23 Dental tourism complication — 1 in 20 Pet parasites — 1 in 20 Undiagnosed ADHD — 1 in 20 Adult-onset food allergy — 1 in 19 Indoor cooking smoke — 1 in 18 Non-Alzheimer's dementia — 1 in 17 Working-age disabling stroke — 1 in 17 Cannabis use disorder — 1 in 16 Stroke — 1 in 15 Parent death/disability — 1 in 14 Severe hearing loss — 1 in 14 Type 2 diabetes — 1 in 13 Appendicitis — 1 in 13 Untreated depression — 1 in 13 Untreated back pain disability — 1 in 13 Heart disease — 1 in 12 Medical error death — 1 in 12 Compulsive sexual behavior — 1 in 12 Eating disorder — 1 in 11 Hip replacement — 1 in 11 Kidney stones — 1 in 11 Sedentary lifestyle — 1 in 11 Salon infection — 1 in 11 Ovarian cancer — 1 in 91 Colorectal cancer — 1 in 77 Breast cancer — 1 in 59 Liver cancer — 1 in 59 Lung cancer — 1 in 56 Prostate cancer — 1 in 50 Melanoma (UV) — 1 in 29 Low-fiber CRC risk — 1 in 23 Red meat & CRC — 1 in 21 Charred meat & cancer — 1 in 20 Maintenance crash — 1 in 83 Driving on sedating meds — 1 in 77 Texting + driving — 1 in 56 Driving after cannabis — 1 in 53 Eating while driving — 1 in 53 Unbelted crash death — 1 in 53 Speeding 20% over limit — 1 in 48 Motorcycle no helmet — 1 in 45 Spaceflight (astronaut) — 1 in 42 Video watching + driving — 1 in 32 Drowsy driving — 1 in 26 E-scooter injury — 1 in 26 Cruise ship norovirus — 1 in 24 Driving at 0.10% BAC — 1 in 16 Catalytic converter theft — 1 in 83 Pickpocketed while traveling — 1 in 38 Stabbed in an assault — 1 in 37 Vehicle theft — 1 in 34 Street robbery / mugging — 1 in 26 Wrongful conviction — 1 in 24 Drink spiking — 1 in 17 Protest under autocracy — 1 in 12 AMOC collapse — 1 in 20 Sting anaphylaxis — 1 in 50 Cat collar injury — 1 in 25 Fish bone injury — 1 in 68 Restaurant food poisoning — 1 in 58 Vegetarian deficiency — 1 in 25 Intimate deepfake — 1 in 25 Social media problematic use — 1 in 13 Infant fall — 1 in 100 Childbirth death (SSA) — 1 in 55 Co-sleeping death — 1 in 43 Toddler stair fall — 1 in 37 Play swing & slide injury — 1 in 33 Autism diagnosis — 1 in 31 C-section complications — 1 in 29 Toy injury requiring ER (child) — 1 in 21 Preeclampsia — 1 in 20 Severe birth tearing — 1 in 17 Gestational diabetes — 1 in 13 Child fall head injury — 1 in 12 Sports betting financial ruin — 1 in 100 Fighter pilot death — 1 in 48 Commercial fishing career death — 1 in 45 Logging career death — 1 in 34 Dying without heir — 1 in 33 Medical bankruptcy — 1 in 25 Compulsive buying disorder — 1 in 20 Rental listing scam loss — 1 in 20 Mortgage foreclosure — 1 in 14 Musculoskeletal LTD claim — 1 in 14 Day-trading 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drowning — 1 in 685 Driver kills pedestrian — 1 in 552 Phone-distracted walking injury — 1 in 400 EV battery fire — 1 in 333 Cyclist killed by car — 1 in 196 Hand-held phone call + driving — 1 in 143 Petrol car fire — 1 in 125 Self-driving car fatality — 1 in 115 Car crash — 1 in 105 Firefighter duty death — 1 in 455 Police duty death — 1 in 313 Homicide — 1 in 287 Pig-butchering scam — 1 in 106 Extreme heat — 1 in 333 Climate change death — 1 in 204 Swallowed bee/wasp — 1 in 500 Bat bite & rabies — 1 in 238 Mosquito-borne disease — 1 in 190 Food poisoning (global) — 1 in 317 Solar panel fire — 1 in 667 Untreated childhood scoliosis — 1 in 1,000 Child window fall — 1 in 855 Walker stair fall — 1 in 625 Baby walker injury — 1 in 455 Maternal mortality — 1 in 272 Untreated childhood flat feet — 1 in 250 Maternal age & birth defects — 1 in 200 Child death (<18) — 1 in 143 Caving career death — 1 in 167 EMS duty death — 1 in 794 Civilian war casualty — 1 in 499 Soldier in combat — 1 in 270 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in 6,536 Blizzard death — 1 in 4,367 Earthquake — 1 in 3,802 Dog chocolate death — 1 in 2,000 Food poisoning (US) — 1 in 1,862 Fish mercury — 1 in 1,695 Phone/laptop battery fire — 1 in 1,136 SIDS — 1 in 7,143 Laundry pod ingestion — 1 in 6,494 Untreated infant hip dysplasia — 1 in 5,000 Pool drowning — 1 in 2,299 War (civilian) — 1 in 2,000 Fatal bee/wasp sting — 1 in 76,923 Anesthesia death — 1 in 50,000 Dog hot car death — 1 in 41,667 Anaphylaxis — 1 in 27,548 Chiropractic neck manipulation — 1 in 16,667 CO poisoning — 1 in 14,006 Hepatitis A (travel) — 1 in 12,500 Skipping allergy immunotherapy — 1 in 11,111 Acrylamide & cancer — 1 in 16,667 Bus crash — 1 in 100,000 Plane crash — 1 in 58,824 Child pedestrian (residential) — 1 in 45,455 Railroad crossing death — 1 in 20,704 Child bike trailer — 1 in 14,286 Acid attack — 1 in 89,286 Terrorism — 1 in 77,519 Child stranger abduction — 1 in 38,760 Stranger kidnapping — 1 in 35,211 Dowry death — 1 in 13,158 Accidental gun death — 1 in 11,299 Wildfire — 1 in 100,000 Tornado — 1 in 80,645 Tsunami — 1 in 52,632 Ocean drowning — 1 in 29,155 Flood — 1 in 20,202 Landslide death — 1 in 18,416 Supervolcano eruption — 1 in 12,376 Crocodile attack — 1 in 84,746 Bee sting — 1 in 78,927 Fatal scorpion sting — 1 in 26,110 Plastic container leaching — 1 in 16,949 Infant in car seat — 1 in 64,935 Bouncer chair fall — 1 in 60,606 Toddler choking — 1 in 50,000 Unsupervised infant choking — 1 in 50,000 Magnet ingestion — 1 in 12,048 Snorkeling death — 1 in 21,739 Pet in transport — 1 in 20,000 Landmine or UXO injury — 1 in 14,728 Vaccine reaction — 1 in 763,359 Aluminum & Alzheimer's — 1 in 169,492 Residential gas leak — 1 in 140,845 Child hot car death — 1 in 102,041 Glyphosate & cancer — 1 in 1,000,000 Teflon cookware cancer — 1 in 169,492 Roller coaster injury — 1 in 312,500 Cruise ship accident — 1 in 188,679 Ferry sinking — 1 in 133,333 Turbulence injury — 1 in 114,943 School shooting — 1 in 192,308 Mass shooting — 1 in 113,636 Nuclear accident — 1 in 833,333 Avalanche — 1 in 210,526 Lightning — 1 in 209,205 Snake bite — 1 in 884,956 Spider bite — 1 in 833,333 Hippo attack — 1 in 564,972 Dog bite — 1 in 142,045 Pesticide residue — 1 in 1,000,000 Dirty can illness — 1 in 200,000 PLA bioplastic harm — 1 in 169,492 Charger left plugged in — 1 in 200,000 Infant swing death — 1 in 714,286 Child blind cord strangulation — 1 in 416,667 Child plastic bag suffocation — 1 in 263,158 Button battery — 1 in 250,000 Inclined sleeper death — 1 in 238,095 Elevator/escalator death — 1 in 188,324 Japanese encephalitis (travel) — 1 in 2,000,000 Kid + front airbag — 1 in 10,000,000 Asteroid impact — 1 in 1,351,351 Banana spider eggs — 1 in 10,000,000 Shark attack — 1 in 5,681,818 Bear attack — 1 in 3,787,879 Wild berry poisoning — 1 in 2,222,222 Space debris hits property — 1 in 10,000,000 Piranha attack — 1 in 135,135,135 Phone at gas pump — 1 in 1,000,000,000 Phone on plane — 1 in 1,000,000,000 Alien contact — 1 in 169,491,525
Lottery jackpot 1 in 95,238