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Perceived fear vs. actual probability

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

Lifetime probability · subgroup

roughly 1 in 20 US online renters lost money to a rental scam at some point

5.0% lifetime chance

Most people underestimate this.

Scopes vary — shown as typical adult lifetime odds. See methodology.

Other · reviewed 2026-05-16
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
Source Government statistic · Federal Trade Commission
lifetime, subgroup each band = 10× rarer → See full scale →
certain 1 in 1K 1 in 1M 1 in 1B
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 nationally representative survey of over 1,000 US renters (1,126 national respondents): an estimated 5.2 million US renters report having lost money to a rental scam. Among renters, 43.1% encountered a listing they suspected was fraudulent and 6.4% lost money (≈5% at the low end used here for self-report noise). 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): tight, high-scarcity rental markets where urgency pressure raises scam efficacy (the source's per-metro loss rates ranged widely, up to ~10-11%).

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 continues to evolve as apartment search moves fully online, which may push rates above the 2018 baseline, but no post-2018 population-level survey re-measures the lifetime loss rate. 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 nationally representative survey of over 1,000 US renters found that 43.1% encountered a listing they suspected was fraudulent, and an estimated 5.2 million US renters — 6.4% of renters, roughly 1 in 20 — 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 people aged 18–29 were three times more likely than other adults to report losing money to a rental scam — 46% of monetary-loss reports came from that group — 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.

The 5% loss-rate baseline comes from a single 2018 survey, and no comparable population-level study has re-measured it since. Because apartment searching has moved almost entirely online — where a fake listing can reuse photos scraped from real ones and reach many renters at low cost — the underlying baseline may understate current risk for first-time renters who lack the pattern-recognition experience to spot inconsistencies. That is a plausibility argument, not a measured figure: absent a newer survey, the 2018 rate remains the best available estimate for the lifetime loss rate among online renters.

Roughly 1 in 20 US adults who search for rentals online report losing money to a fake-listing scam at some point. Far more encounter a suspicious listing and walk away; the losers tend to be those under acute housing pressure, where verifying a listing is costly.

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 — Million Dollar Scam: Rental Fraud Costs 5.2 Million U.S. Renters
    Million Dollar Scam: Rental Fraud Costs 5.2 Million U.S. Renters
    Statistic
    43.1% of renters encountered a listing they suspected was fraudulent; 6.4% of US renters (an estimated 5.2 million) lost money on a rental scam; 9.1% of renters aged 18–29 lost money
    Excerpt
    “"An estimated 43.1 percent of renters have encountered a listing they suspected was fraudulent, and 5.2 million U.S. renters have lost money from rental fraud. [...] 6.4 percent of U.S. renters lost money on a rental scam. [...] younger renters are more likely to experience rental fraud, with 9.1 percent of 18 to 29-year-old renters having lost money on a rental scam, compared to 6.4 percent of all renters." Based on "a nationally representative survey of over 1,000 U.S. renters." ”
    Source data from
    2018-04-25
    Accessed
    2026-07-01 · archived copy
    Calculation
    Apartment List rental-fraud report (nationally representative survey of over 1,000 US renters). The 5.2M lifetime-loss figure equals the report's 6.4% money-loss rate among US renters. The 43.1% "encountered a 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 reported 6.4% money-loss rate is the basis for the native/normalized rate (rounded to ~5% at the low end of the reported range in this entry, given self-report noise); 18–29 renters lost money at 9.1%, nearly 1.5× the all-renter rate.
  2. [2] Federal Trade Commission — Data Spotlight: Rental scams hit home with $65 million in reported losses
    Data Spotlight: Rental scams hit home with $65 million in reported losses
    Statistic
    Since 2020, nearly 65,000 rental scams reported to the FTC with about $65 million in losses; median reported loss $1,000; people aged 18–29 were three times more likely than other adults to report losing money to a rental scam
    Excerpt
    “"Since 2020, people have reported nearly 65,000 rental scams to the FTC with about $65 million in losses. [...] People ages 18 to 29 were three times more likely than other adults to report losing money to a rental scam. [...] The median reported loss amount during this period was $1,000." ”
    Source data from
    2025-12-22
    Accessed
    2026-06-30 · archived copy
    Calculation
    FTC Consumer Sentinel Network Data Spotlight, December 22, 2025. Aggregates rental-scam reports 2020–2025: ~65,000 reports, ~$65M reported losses, $1,000 median loss. Reports are a reported-only numerator with no population denominator, so this does not independently supply the 5% lifetime rate; the Apartment List survey remains the primary source for the native estimate. Used as corroboration that rental fraud is a documented, non-trivial category that disproportionately affects young adults — those aged 18–29 were three times more likely than other adults to report losing money to a rental scam, and 46% of monetary-loss reports came from the 18–29 group.
  3. [3] Federal Bureau of Investigation Internet Crime Complaint Center (FBI IC3) — Internet Crime Report 2023
    Internet Crime Report 2023
    Statistic
    In 2023 the FBI IC3 received 9,521 Real Estate complaints with about $145 million in losses; the IC3 'Real Estate' category is defined as 'Loss of funds from a real estate investment or fraud involving rental or timeshare property'
    Excerpt
    “"Real Estate: Loss of funds from a real estate investment or fraud involving rental or timeshare property." In 2023 the IC3 recorded 9,521 Real Estate complaints and approximately $145 million in reported losses under this crime-type category. ”
    Source data from
    2024-03-01
    Accessed
    2026-07-01 · archived copy
    Calculation
    FBI IC3 2023 Internet Crime Report, "Real Estate" crime type (9,521 complaints, ~$145M reported losses). The IC3 "Real Estate" category covers real-estate investment loss plus rental and timeshare fraud, so it is broader than online fake-listing rental scams alone. The $145M loss figure is a reported-only numerator; most rental scam victims do not file IC3 complaints, so it supplies no population denominator. Used only as independent confirmation that rental-related fraud is a substantial, documented category.

434 risks with measured probability
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— 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 2.3 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 9.8 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 Iron gap (women) — 1 in 3.9 Vitamin D gap — 1 in 2.9 Magnesium gap — 1 in 1.9 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 Child head lice — 1 in 2.0 Elder fraud loss — 1 in 10 Pension fund collapse — 1 in 10 Housing crash — 1 in 8.3 IRS audit — 1 in 6.7 Currency collapse — 1 in 5.6 Visa overstay deportation — 1 in 5.6 Subprime auto-loan repossession — 1 in 5.0 Long term disability working age — 1 in 4.0 Student loan default — 1 in 3.8 Whistleblower retaliation — 1 in 3.2 Forced job exit before retirement — 1 in 2.9 Retirement shortfall — 1 in 2.6 BNPL missed payment — 1 in 2.4 Divorce — 1 in 2.4 Burst pipe damage — 1 in 2.2 Workplace bullying — 1 in 2.1 Prolonged grid blackout — 1 in 2.0 Deportation (undocumented) — 1 in 1.8 Funeral cost shock — 1 in 1.7 Identity theft — 1 in 1.7 Credit card fraud — 1 in 1.5 School bullying — 1 in 1.5 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 Problem-tenant loss — 1 in 1.8 LASIK complications — 1 in 100 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 Drug overdose — 1 in 56 Gas-stove asthma in a child — 1 in 50 Cat litter toxoplasmosis — 1 in 48 Mental health LTD claim — 1 in 45 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 Wisdom-tooth surgery — 1 in 22 Dental tourism complication — 1 in 20 Elderly abandonment — 1 in 20 Parkinson's — 1 in 20 Pet parasites — 1 in 20 Undiagnosed ADHD — 1 in 20 Adult-onset food allergy — 1 in 19 Non-Alzheimer's dementia — 1 in 17 Cannabis use disorder — 1 in 16 Stroke — 1 in 15 PTSD — 1 in 15 Severe hearing loss — 1 in 14 Type 2 diabetes — 1 in 13 Appendicitis — 1 in 13 Indoor cooking smoke — 1 in 13 Untreated depression — 1 in 13 Soda and diabetes — 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 Parent death/disability — 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 26 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 Unbelted crash death — 1 in 53 Speeding 20% over limit — 1 in 50 Motorcycle no helmet — 1 in 45 Spaceflight (astronaut) — 1 in 42 Video watching + driving — 1 in 32 Child crash injury — 1 in 27 Drowsy driving — 1 in 26 Cruise ship norovirus — 1 in 24 E-scooter injury — 1 in 10 Pickpocketed while traveling — 1 in 38 Catalytic converter theft — 1 in 37 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career death — 1 in 45 Logging career death — 1 in 34 Medical bankruptcy — 1 in 25 Compulsive buying disorder — 1 in 20 Rental listing scam loss — 1 in 20 Losing SNAP under 2025 work rules — 1 in 18 Mortgage foreclosure — 1 in 14 Musculoskeletal LTD claim — 1 in 14 Day-trading losses — 1 in 13 Extremist govt catastrophe — 1 in 13 Hurricane home destruction — 1 in 17 NAION (Ozempic) — 1 in 909 Infant pool submersion — 1 in 800 MS — 1 in 769 Workplace fatality — 1 in 690 Typhoid fever — 1 in 654 GLP-1 anesthesia aspiration — 1 in 613 Unsafe imported products — 1 in 565 Brain aneurysm — 1 in 400 COVID-19 — 1 in 400 Fireworks injury — 1 in 385 Too much caffeine — 1 in 366 Sickle cell disease — 1 in 365 Counterfeit medicine — 1 in 361 Spinal cord injury — 1 in 313 Childhood cancer diagnosis — 1 in 285 Next pandemic death — 1 in 208 Dengue (travel) — 1 in 200 Heat-triggered preterm birth — 1 in 200 Skipping daily showers — 1 in 200 Not scrubbing feet — 1 in 200 Marrow donation risk — 1 in 167 Tick-borne encephalitis — 1 in 167 Schizophrenia — 1 in 143 Accidental fall — 1 in 135 Sudden death during exercise — 1 in 123 Suicide (US) — 1 in 121 Opioid addiction — 1 in 114 Tuberculosis (global) — 1 in 109 HIV diagnosis — 1 in 105 Radon cancer — 1 in 435 Testicular cancer — 1 in 250 Cervical cancer — 1 in 167 Pancreatic cancer — 1 in 125 Pedestrian death — 1 in 806 Motorcycle crash — 1 in 709 Boating 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 159 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 357 Homicide — 1 in 339 Pig-butchering scam — 1 in 106 Extreme heat — 1 in 333 Climate change death — 1 in 204 Bat bite & rabies — 1 in 278 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 Infant fall — 1 in 500 Baby walker injury — 1 in 455 Maternal mortality — 1 in 272 Maternal age & birth defects — 1 in 200 Child death (<18) — 1 in 103 Caving career death — 1 in 167 EMS duty death — 1 in 909 Civilian war casualty — 1 in 499 Soldier in combat — 1 in 270 Student visa revocation — 1 in 263 Mining career death — 1 in 214 Gambling financial ruin — 1 in 159 Lightning home fire — 1 in 461 Wildfire home destruction — 1 in 120 Malaria (travel) — 1 in 10,000 Infection from shared drink — 1 in 10,000 Chagas disease — 1 in 8,475 Wild berry fox tapeworm — 1 in 8,475 Child nicotine-pouch ingestion — 1 in 7,937 Schistosomiasis death — 1 in 6,667 Sudden death (young adult) — 1 in 3,922 Unsafe wiring — 1 in 3,390 Sepsis from wound — 1 in 2,857 Anesthesia awareness — 1 in 2,500 Heat stroke (outdoor) — 1 in 1,905 House fire — 1 in 1,818 Rabies from dogs — 1 in 1,449 Drowning — 1 in 1,379 Shallow-water diving SCI — 1 in 1,111 Choking — 1 in 1,099 EVALI vaping hospitalization — 1 in 1,064 Betel nut cancer — 1 in 1,290 Blood clot (flight) — 1 in 4,651 Killing a cyclist — 1 in 3,937 Teen road-crash death — 1 in 3,030 Child rear bike seat — 1 in 2,500 Child without restraint — 1 in 2,000 Fatal police encounter — 1 in 4,739 Honor killing — 1 in 2,381 Intimate-partner homicide — 1 in 1,767 Hurricane — 1 in 8,929 Drought famine death — 1 in 6,536 Blizzard death — 1 in 4,367 Earthquake — 1 in 3,802 Listeria from deli meat — 1 in 6,061 Serious E. coli from fresh produce — 1 in 4,831 Food poisoning (US) — 1 in 1,862 Fish mercury — 1 in 1,695 Fish bone injury — 1 in 1,429 Phone/laptop battery fire — 1 in 4,545 Laundry pod ingestion — 1 in 6,494 Pool drowning — 1 in 5,882 Untreated infant hip dysplasia — 1 in 5,000 SIDS — 1 in 2,398 War (civilian) — 1 in 2,000 Flu brain swelling in a child (IAE/ANE) — 1 in 100,000 Fatal bee/wasp sting — 1 in 76,923 Locally-acquired dengue (continental US) — 1 in 66,667 Anesthesia death — 1 in 45,662 Dog hot car death — 1 in 41,667 Vibrio vulnificus wound infection — 1 in 32,051 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 Post-crash car fire — 1 in 25,000 Railroad crossing death — 1 in 20,576 Car submersion — 1 in 16,667 Child bike trailer — 1 in 14,286 Runway near-miss — 1 in 13,699 Acid attack — 1 in 94,340 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 Post-hurricane heat death — 1 in 20,000 Landslide death — 1 in 18,416 Supervolcano eruption — 1 in 12,376 Bee sting — 1 in 78,927 Swallowed bee/wasp — 1 in 29,155 Fatal scorpion sting — 1 in 26,110 Dog chocolate death — 1 in 13,889 Lead-tainted cinnamon pouch — 1 in 40,000 Plastic container leaching — 1 in 16,949 Infant car-seat asphyxia — 1 in 64,935 Bouncer chair fall — 1 in 60,606 Toddler choking — 1 in 50,000 Unsupervised infant choking — 1 in 50,000 Forward-facing toddler death — 1 in 26,738 Magnet ingestion — 1 in 12,048 Snorkeling death — 1 in 21,739 Pet in transport — 1 in 20,000 Death in ICE custody — 1 in 17,065 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 Ferry sinking — 1 in 133,333 Turbulence injury — 1 in 114,943 School shooting — 1 in 192,308 Mass shooting — 1 in 113,636 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 Crocodile attack — 1 in 337,838 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 Infant swing death — 1 in 714,286 Whole-grape choking — 1 in 625,000 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 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
Lottery jackpot 1 in 95,238

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