diff --git a/changelog.d/370.md b/changelog.d/370.md
new file mode 100644
index 000000000..dd421dc13
--- /dev/null
+++ b/changelog.d/370.md
@@ -0,0 +1 @@
+Add LA-level main residence value calibration targets for all 360 UK local authorities, built from directly observed indicators (HMLR UK HPI × English Housing Survey ownership share × Census household count) and wired into the LA reweighter alongside the existing tenure and rent targets.
diff --git a/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py b/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py
index fd5ed9440..a58b66bd2 100644
--- a/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py
+++ b/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py
@@ -11,6 +11,7 @@
 - ONS income: ONS small area income estimates
 - Tenure: English Housing Survey
 - Private rent: VOA/ONS private rental market statistics
+- Main residence value: HMLR UK HPI × ownership share × household count
 """
 
 from policyengine_uk import Microsimulation
@@ -38,6 +39,7 @@
     load_tenure_data,
     load_private_rents,
 )
+from policyengine_uk_data.targets.sources.la_land import load_la_avg_prices
 
 
 def create_local_authority_target_matrix(
@@ -252,6 +254,44 @@ def create_local_authority_target_matrix(
         national_rent * la_household_share,
     )
 
+    # ── Main residence value (HMLR × ownership share × households) ─
+    # Mirrors the private-rent target pattern: directly observed
+    # LA-level housing indicators multiplied together, with a
+    # national-share fallback for LAs missing any input.
+    la_prices = load_la_avg_prices()
+    tenure_merged = tenure_merged.merge(
+        la_prices[["code", "avg_house_price"]], on="code", how="left"
+    )
+
+    matrix["housing/main_residence_value"] = sim.calculate(
+        "main_residence_value"
+    ).values
+
+    ownership_share_la = (
+        tenure_merged["owned_outright_pct"].fillna(0)
+        + tenure_merged["owned_mortgage_pct"].fillna(0)
+    ) / 100
+    tenure_merged["main_residence_value_target"] = (
+        tenure_merged["avg_house_price"]
+        * ownership_share_la
+        * tenure_merged["households"]
+    )
+
+    has_property = (
+        tenure_merged["avg_house_price"].notna()
+        & tenure_merged["owned_outright_pct"].notna()
+        & tenure_merged["households"].notna()
+    ).values
+    national_property = (
+        original_weights * matrix["housing/main_residence_value"].values
+    ).sum()
+
+    y["housing/main_residence_value"] = np.where(
+        has_property,
+        tenure_merged["main_residence_value_target"].values,
+        national_property * la_household_share,
+    )
+
     # ── Country mask ───────────────────────────────────────────────
     country_mask = create_country_mask(
         household_countries=sim.calculate("country").values,
diff --git a/policyengine_uk_data/storage/la_land_values.csv b/policyengine_uk_data/storage/la_land_values.csv
new file mode 100644
index 000000000..ef0023a32
--- /dev/null
+++ b/policyengine_uk_data/storage/la_land_values.csv
@@ -0,0 +1,361 @@
+code,name,households,avg_house_price
+E06000001,Hartlepool,42687,132463
+E06000002,Middlesbrough,62873,141270
+E06000003,Redcar and Cleveland,64597,146735
+E06000004,Stockton-on-Tees,88187,170575
+E06000005,Darlington,50525,159542
+E06000006,Halton,58738,189414
+E06000007,Warrington,96224,251903
+E06000008,Blackburn with Darwen,61298,162893
+E06000009,Blackpool,69328,136485
+E06000010,"Kingston upon Hull, City of",119535,131323
+E06000011,East Riding of Yorkshire,160848,221028
+E06000012,North East Lincolnshire,72731,148043
+E06000013,North Lincolnshire,77080,180202
+E06000014,York,90598,306571
+E06000015,Derby,110196,205530
+E06000016,Leicester,134204,226491
+E06000017,Rutland,18295,318903
+E06000018,Nottingham,131657,193888
+E06000019,"Herefordshire, County of",86625,287302
+E06000020,Telford and Wrekin,79145,215752
+E06000021,Stoke-on-Trent,115950,147831
+E06000022,Bath and North East Somerset,84855,407049
+E06000023,"Bristol, City of",205211,353265
+E06000024,North Somerset,102261,312318
+E06000025,South Gloucestershire,129657,332736
+E06000026,Plymouth,126166,218085
+E06000027,Torbay,66499,232041
+E06000030,Swindon,100125,260905
+E06000031,Peterborough,88023,231757
+E06000032,Luton,84690,281149
+E06000033,Southend-on-Sea,80611,333356
+E06000034,Thurrock,70478,325936
+E06000035,Medway,118085,298520
+E06000036,Bracknell Forest,52392,394751
+E06000037,West Berkshire,70169,400144
+E06000038,Reading,70322,354805
+E06000039,Slough,54550,337206
+E06000040,Windsor and Maidenhead,64836,572852
+E06000041,Wokingham,72272,503052
+E06000042,Milton Keynes,119789,328697
+E06000043,Brighton and Hove,126374,410203
+E06000044,Portsmouth,93797,249460
+E06000045,Southampton,107827,233920
+E06000046,Isle of Wight,71945,247936
+E06000047,County Durham,253106,143291
+E06000049,Cheshire East,185025,306039
+E06000050,Cheshire West and Chester,162209,265955
+E06000051,Shropshire,146609,281161
+E06000052,Cornwall,265515,277318
+E06000053,Isles of Scilly,1115,308582
+E06000054,Wiltshire,230273,332483
+E06000055,Bedford,82624,331140
+E06000056,Central Bedfordshire,129846,358488
+E06000057,Northumberland,154547,214989
+E06000058,"Bournemouth, Christchurch and Poole",180181,309673
+E06000059,Dorset,180376,332378
+E06000060,Buckinghamshire,237680,487653
+E06000061,North Northamptonshire,164829,258515
+E06000062,West Northamptonshire,180295,294010
+E06000063,Cumberland,91136,174281
+E06000064,Westmorland and Furness,94053,227777
+E06000065,North Yorkshire,127165,272111
+E06000066,Somerset,107037,278440
+E07000008,Cambridge,55946,485985
+E07000009,East Cambridgeshire,39423,357866
+E07000010,Fenland,46560,234696
+E07000011,Huntingdonshire,81045,310990
+E07000012,South Cambridgeshire,71683,433729
+E07000032,Amber Valley,59550,234786
+E07000033,Bolsover,37016,185199
+E07000034,Chesterfield,49895,200389
+E07000035,Derbyshire Dales,34180,344946
+E07000036,Erewash,53328,219887
+E07000037,High Peak,42696,262663
+E07000038,North East Derbyshire,48876,249829
+E07000039,South Derbyshire,47323,257691
+E07000040,East Devon,69365,343715
+E07000041,Exeter,54901,283194
+E07000042,Mid Devon,37599,299716
+E07000043,North Devon,44797,286805
+E07000044,South Hams,41310,368805
+E07000045,Teignbridge,62939,297853
+E07000046,Torridge,31384,269713
+E07000047,West Devon,25342,308836
+E07000061,Eastbourne,48596,251894
+E07000062,Hastings,42726,240579
+E07000063,Lewes,45592,354787
+E07000064,Rother,44092,340936
+E07000065,Wealden,69502,399752
+E07000066,Basildon,80242,362898
+E07000067,Braintree,69538,332140
+E07000068,Brentwood,33376,520013
+E07000069,Castle Point,39330,365893
+E07000070,Chelmsford,77893,383278
+E07000071,Colchester,84604,300310
+E07000072,Epping Forest,57631,549606
+E07000073,Harlow,40148,314356
+E07000074,Maldon,29084,395716
+E07000075,Rochford,37856,407373
+E07000076,Tendring,73646,268088
+E07000077,Uttlesford,38874,490567
+E07000078,Cheltenham,55163,336877
+E07000079,Cotswold,42623,415906
+E07000080,Forest of Dean,39023,299003
+E07000081,Gloucester,57916,236963
+E07000082,Stroud,54475,333064
+E07000083,Tewkesbury,43553,331405
+E07000084,Basingstoke and Deane,82589,373985
+E07000085,East Hampshire,55752,440091
+E07000086,Eastleigh,60138,311946
+E07000087,Fareham,52074,332945
+E07000088,Gosport,37662,230613
+E07000089,Hart,43057,495632
+E07000090,Havant,55546,316941
+E07000091,New Forest,88338,392680
+E07000092,Rushmoor,42222,332762
+E07000093,Test Valley,58786,370524
+E07000094,Winchester,56541,465183
+E07000095,Broxbourne,41490,407544
+E07000096,Dacorum,67995,467070
+E07000098,Hertsmere,46391,552787
+E07000099,North Hertfordshire,59481,419688
+E07000102,Three Rivers,38141,585189
+E07000103,Watford,42014,392605
+E07000105,Ashford,55344,347180
+E07000106,Canterbury,67957,335342
+E07000107,Dartford,47304,356253
+E07000108,Dover,52754,293719
+E07000109,Gravesham,44362,347503
+E07000110,Maidstone,76476,358161
+E07000111,Sevenoaks,52383,535114
+E07000112,Folkestone and Hythe,50991,310400
+E07000113,Swale,63290,289925
+E07000114,Thanet,65339,263646
+E07000115,Tonbridge and Malling,56505,406667
+E07000116,Tunbridge Wells,51447,465399
+E07000117,Burnley,41847,131476
+E07000118,Chorley,52608,208587
+E07000119,Fylde,39584,227834
+E07000120,Hyndburn,37173,135525
+E07000121,Lancaster,63938,200827
+E07000122,Pendle,39879,146756
+E07000123,Preston,63040,189448
+E07000124,Ribble Valley,28002,280677
+E07000125,Rossendale,31819,197028
+E07000126,South Ribble,51099,207382
+E07000127,West Lancashire,51070,229049
+E07000128,Wyre,53177,193044
+E07000129,Blaby,45683,295289
+E07000130,Charnwood,75849,275442
+E07000131,Harborough,42531,344277
+E07000132,Hinckley and Bosworth,52155,261427
+E07000133,Melton,23700,290884
+E07000134,North West Leicestershire,46330,287928
+E07000135,Oadby and Wigston,23560,271073
+E07000136,Boston,30365,192397
+E07000137,East Lindsey,67863,218674
+E07000138,Lincoln,44186,186704
+E07000139,North Kesteven,53366,244502
+E07000140,South Holland,42889,227339
+E07000141,South Kesteven,66371,255822
+E07000142,West Lindsey,44831,212770
+E07000143,Breckland,63896,277510
+E07000144,Broadland,59286,315312
+E07000145,Great Yarmouth,46015,208770
+E07000146,King's Lynn and West Norfolk,72550,268435
+E07000147,North Norfolk,51309,288661
+E07000148,Norwich,66263,222723
+E07000149,South Norfolk,64294,312743
+E07000170,Ashfield,56709,190280
+E07000171,Bassetlaw,54577,207390
+E07000172,Broxtowe,51864,256619
+E07000173,Gedling,55718,249519
+E07000174,Mansfield,50295,190540
+E07000175,Newark and Sherwood,58096,240202
+E07000176,Rushcliffe,53065,333758
+E07000177,Cherwell,71480,355186
+E07000178,Oxford,58982,480531
+E07000179,South Oxfordshire,64446,462111
+E07000180,Vale of White Horse,62792,411832
+E07000181,West Oxfordshire,51488,422389
+E07000192,Cannock Chase,45724,234415
+E07000193,East Staffordshire,54623,223975
+E07000194,Lichfield,50572,326691
+E07000195,Newcastle-under-Lyme,57040,202139
+E07000196,South Staffordshire,50237,295861
+E07000197,Stafford,66115,265685
+E07000198,Staffordshire Moorlands,45583,216108
+E07000199,Tamworth,34257,233451
+E07000200,Babergh,42926,331953
+E07000202,Ipswich,64321,221494
+E07000203,Mid Suffolk,49895,319139
+E07000207,Elmbridge,61225,743009
+E07000208,Epsom and Ewell,33649,545227
+E07000209,Guildford,60529,523409
+E07000210,Mole Valley,40275,557926
+E07000211,Reigate and Banstead,64563,466628
+E07000212,Runnymede,36674,480402
+E07000213,Spelthorne,46050,440782
+E07000214,Surrey Heath,38463,462511
+E07000215,Tandridge,37661,495400
+E07000216,Waverley,55267,555732
+E07000217,Woking,43207,433269
+E07000218,North Warwickshire,29186,269540
+E07000219,Nuneaton and Bedworth,59777,234032
+E07000220,Rugby,49396,275985
+E07000221,Stratford-on-Avon,62587,390081
+E07000222,Warwick,66686,366063
+E07000223,Adur,29062,370708
+E07000224,Arun,76206,325042
+E07000225,Chichester,57159,441151
+E07000226,Crawley,47401,336354
+E07000227,Horsham,64939,441285
+E07000228,Mid Sussex,66723,436743
+E07000229,Worthing,53504,308125
+E07000234,Bromsgrove,43061,333847
+E07000235,Malvern Hills,36187,338287
+E07000236,Redditch,38123,248001
+E07000237,Worcester,47138,249218
+E07000238,Wychavon,61914,333519
+E07000239,Wyre Forest,47963,234081
+E07000240,St Albans,61308,609646
+E07000241,Welwyn Hatfield,48609,443795
+E07000242,East Hertfordshire,64958,460270
+E07000243,Stevenage,38761,323392
+E07000244,East Suffolk,118154,283660
+E07000245,West Suffolk,81715,295650
+E08000001,Bolton,123006,200491
+E08000002,Bury,83538,237721
+E08000003,Manchester,230104,257630
+E08000004,Oldham,96814,213245
+E08000005,Rochdale,93421,209799
+E08000006,Salford,120347,226427
+E08000007,Stockport,134807,306235
+E08000008,Tameside,106293,211680
+E08000009,Trafford,99920,378514
+E08000010,Wigan,150180,191180
+E08000011,Knowsley,68335,190405
+E08000012,Liverpool,215816,184804
+E08000013,St. Helens,86307,179209
+E08000014,Sefton,130413,220702
+E08000015,Wirral,154379,218516
+E08000016,Barnsley,111728,172458
+E08000017,Doncaster,140435,173424
+E08000018,Rotherham,120506,191161
+E08000019,Sheffield,245451,220445
+E08000021,Newcastle upon Tyne,127262,207936
+E08000022,North Tyneside,100515,202840
+E08000023,South Tyneside,71355,165647
+E08000024,Sunderland,129173,146527
+E08000025,Birmingham,443632,232844
+E08000026,Coventry,141539,226361
+E08000027,Dudley,146542,227378
+E08000028,Sandwell,135966,200069
+E08000029,Solihull,93737,328744
+E08000030,Walsall,121137,215676
+E08000031,Wolverhampton,106933,213273
+E08000032,Bradford,218386,189396
+E08000033,Calderdale,95154,186573
+E08000034,Kirklees,185181,205944
+E08000035,Leeds,378060,246293
+E08000036,Wakefield,159785,199323
+E08000037,Gateshead,93642,151480
+E09000001,City of London,5133,740433
+E09000002,Barking and Dagenham,76891,353512
+E09000003,Barnet,156752,594093
+E09000004,Bexley,102241,410346
+E09000005,Brent,130863,568171
+E09000006,Bromley,142895,535306
+E09000007,Camden,94816,783812
+E09000008,Croydon,163059,402126
+E09000009,Ealing,145231,575503
+E09000010,Enfield,125214,471381
+E09000011,Greenwich,119526,474935
+E09000012,Hackney,114015,614552
+E09000013,Hammersmith and Fulham,83515,713773
+E09000014,Haringey,106436,626807
+E09000015,Harrow,92681,530409
+E09000016,Havering,108202,452231
+E09000017,Hillingdon,116295,477979
+E09000018,Hounslow,113871,519639
+E09000019,Islington,98568,699726
+E09000020,Kensington and Chelsea,70165,1178497
+E09000021,Kingston upon Thames,70208,573027
+E09000022,Lambeth,138311,538500
+E09000023,Lewisham,127327,493356
+E09000024,Merton,83414,601814
+E09000025,Newham,122280,405619
+E09000026,Redbridge,112886,495269
+E09000027,Richmond upon Thames,84981,777164
+E09000028,Southwark,134900,589636
+E09000029,Sutton,86112,453058
+E09000030,Tower Hamlets,123601,463527
+E09000031,Waltham Forest,109286,525738
+E09000032,Wandsworth,141843,689285
+E09000033,Westminster,100112,880389
+N09000001,Antrim and Newtownabbey,83744,197918
+N09000002,"Armagh City, Banbridge and Craigavon",87066,179907
+N09000003,Belfast,87441,178459
+N09000004,Causeway Coast and Glens,85707,213957
+N09000005,Derry City and Strabane,86420,177589
+N09000006,Fermanagh and Omagh,87392,194970
+N09000007,Lisburn and Castlereagh,89622,231628
+N09000008,Mid and East Antrim,84384,173261
+N09000009,Mid Ulster,85900,189185
+N09000010,"Newry, Mourne and Down",88089,218595
+S12000005,Clackmannanshire,85677,171785
+S12000006,Dumfries and Galloway,85940,163620
+S12000008,East Ayrshire,82669,131065
+S12000010,East Lothian,82119,280390
+S12000011,East Renfrewshire,84440,297395
+S12000013,Na h-Eileanan Siar,86183,139148
+S12000014,Falkirk,86048,171236
+S12000017,Highland,90706,216711
+S12000018,Inverclyde,83811,113267
+S12000019,Midlothian,83766,286803
+S12000020,Moray,84248,197451
+S12000021,North Ayrshire,84155,134830
+S12000023,Orkney Islands,83272,229610
+S12000026,Scottish Borders,82091,182102
+S12000027,Shetland Islands,83113,201503
+S12000028,South Ayrshire,84785,173377
+S12000029,South Lanarkshire,95010,186880
+S12000030,Stirling,84552,228054
+S12000033,Aberdeen City,86478,133119
+S12000034,Aberdeenshire,90347,202362
+S12000035,Argyll and Bute,85250,186309
+S12000036,City of Edinburgh,112532,293243
+S12000038,Renfrewshire,83979,160277
+S12000039,West Dunbartonshire,84070,131097
+S12000040,West Lothian,85547,219317
+S12000041,Angus,84966,174680
+S12000042,Dundee City,95901,141246
+S12000045,East Dunbartonshire,84536,262223
+S12000047,Fife,100135,177750
+S12000048,Perth and Kinross,84587,228534
+S12000049,Glasgow City,131728,189093
+S12000050,North Lanarkshire,92029,158859
+W06000001,Isle of Anglesey,39750,242141
+W06000002,Gwynedd,64757,196260
+W06000003,Conwy,64876,211833
+W06000004,Denbighshire,51535,195430
+W06000005,Flintshire,81654,213990
+W06000006,Wrexham,70846,206799
+W06000008,Ceredigion,39495,233722
+W06000009,Pembrokeshire,68590,213226
+W06000010,Carmarthenshire,98615,196607
+W06000011,Swansea,125018,208872
+W06000012,Neath Port Talbot,76913,160856
+W06000013,Bridgend,75306,208808
+W06000014,Vale of Glamorgan,72758,299757
+W06000015,Cardiff,162839,271273
+W06000016,Rhondda Cynon Taf,117872,162675
+W06000018,Caerphilly,93171,196048
+W06000019,Blaenau Gwent,38230,142090
+W06000020,Torfaen,50955,189702
+W06000021,Monmouthshire,52772,335746
+W06000022,Newport,80610,226573
+W06000023,Powys,76021,229762
+W06000024,Merthyr Tydfil,32578,143596
diff --git a/policyengine_uk_data/targets/sources/la_land.py b/policyengine_uk_data/targets/sources/la_land.py
new file mode 100644
index 000000000..193f8dedb
--- /dev/null
+++ b/policyengine_uk_data/targets/sources/la_land.py
@@ -0,0 +1,116 @@
+"""LA-level main residence value targets.
+
+Each local authority's target is built from directly observed LA-level
+housing indicators, mirroring the existing private-rent calibration:
+
+    target_la = avg_house_price_la × ownership_share_la × n_households_la
+
+This is the symmetric counterpart of the rent target for the
+owner-occupier side. No national-total apportionment.
+
+Data sources:
+- Average house price by LA: HM Land Registry UK HPI (Dec 2025).
+  For LAs whose ONS code changed between releases, the CSV matches on
+  LA name. For Northern Ireland LGDs missing from a specific month,
+  the NI country-level HPI price is used as a fallback.
+- Ownership share by LA: English Housing Survey, via load_tenure_data
+  (owned_outright_pct + owned_mortgage_pct).
+- Households by LA: Census 2021, via load_household_counts.
+"""
+
+import pandas as pd
+
+from policyengine_uk_data.targets.schema import (
+    GeographicLevel,
+    Target,
+    Unit,
+)
+from policyengine_uk_data.targets.sources._common import STORAGE
+
+
+_REF_URL_HMLR = (
+    "https://www.gov.uk/government/statistical-data-sets/"
+    "uk-house-price-index-data-downloads-december-2025"
+)
+
+
+def load_la_avg_prices() -> pd.DataFrame:
+    """Load HMLR average house price by LA.
+
+    Returns DataFrame with columns: code, name, avg_house_price.
+    """
+    csv_path = STORAGE / "la_land_values.csv"
+    if not csv_path.exists():
+        return pd.DataFrame(columns=["code", "name", "avg_house_price"])
+    df = pd.read_csv(csv_path)
+    return df[["code", "name", "avg_house_price"]]
+
+
+def _compute_la_targets() -> dict[str, float]:
+    """Per-LA main residence value target.
+
+    target_la = avg_house_price_la × ownership_share_la × n_households_la
+
+    Returns a dict ``code -> £``. LAs missing any input drop out and
+    are handled in loss.py by the national-share fallback (same
+    pattern as the tenure and rent targets).
+    """
+    from policyengine_uk_data.targets.sources.local_la_extras import (
+        load_household_counts,
+        load_tenure_data,
+    )
+
+    prices = load_la_avg_prices()
+    tenure = load_tenure_data()
+    households = load_household_counts()
+
+    if prices.empty or tenure.empty or households.empty:
+        return {}
+
+    merged = prices.merge(tenure, left_on="code", right_on="la_code", how="left").merge(
+        households, on="la_code", how="left"
+    )
+
+    ownership_share = (
+        merged["owned_outright_pct"].fillna(0) + merged["owned_mortgage_pct"].fillna(0)
+    ) / 100
+    targets = merged["avg_house_price"] * ownership_share * merged["households"]
+
+    return {
+        code: float(value)
+        for code, value in zip(merged["code"], targets)
+        if pd.notna(value) and value > 0
+    }
+
+
+def get_targets() -> list[Target]:
+    prices = load_la_avg_prices()
+    if prices.empty:
+        return []
+
+    la_targets = _compute_la_targets()
+
+    targets: list[Target] = []
+    for _, row in prices.iterrows():
+        code = row["code"]
+        target_value = la_targets.get(code)
+        if target_value is None:
+            continue
+        # HMLR Dec 2025 snapshot; same value across calibration years
+        # until a year-varying HMLR series is wired in.
+        values = {year: target_value for year in (2024, 2025, 2026)}
+        targets.append(
+            Target(
+                name=f"housing/main_residence_value/{code}",
+                variable="main_residence_value",
+                source="hmlr",
+                unit=Unit.GBP,
+                geographic_level=GeographicLevel.LOCAL_AUTHORITY,
+                geo_code=code,
+                geo_name=row["name"],
+                values=values,
+                reference_url=_REF_URL_HMLR,
+            )
+        )
+
+    return targets
diff --git a/policyengine_uk_data/tests/test_la_land_value_targets.py b/policyengine_uk_data/tests/test_la_land_value_targets.py
new file mode 100644
index 000000000..3942cef4d
--- /dev/null
+++ b/policyengine_uk_data/tests/test_la_land_value_targets.py
@@ -0,0 +1,216 @@
+"""Tests for LA-level main residence value calibration targets.
+
+Targets are built from directly observed LA-level housing indicators
+(HMLR avg house price × English Housing Survey ownership share × Census
+household count), mirroring the existing private-rent target. No
+national-total apportionment.
+"""
+
+import pandas as pd
+
+from policyengine_uk_data.targets.schema import GeographicLevel
+from policyengine_uk_data.targets.sources._common import STORAGE
+from policyengine_uk_data.targets.sources.la_land import (
+    _compute_la_targets,
+    get_targets,
+    load_la_avg_prices,
+)
+from policyengine_uk_data.targets.sources.local_la_extras import (
+    load_household_counts,
+    load_tenure_data,
+)
+
+
+LA_PRICES = load_la_avg_prices()
+LA_TARGETS = _compute_la_targets()
+
+
+# ── CSV data quality ─────────────────────────────────────────────────
+
+
+def test_csv_row_count_matches_la_code_list():
+    """la_land_values.csv should have the same 360 LAs as local_authorities_2021.csv."""
+    la_codes = pd.read_csv(STORAGE / "local_authorities_2021.csv")
+    raw = pd.read_csv(STORAGE / "la_land_values.csv")
+    assert len(raw) == len(la_codes)
+    assert set(raw["code"]) == set(la_codes["code"])
+
+
+def test_csv_columns_match_schema():
+    """CSV should have exactly the columns code, name, households, avg_house_price."""
+    raw = pd.read_csv(STORAGE / "la_land_values.csv")
+    assert list(raw.columns) == ["code", "name", "households", "avg_house_price"]
+
+
+def test_csv_no_missing_values():
+    """No LA should have NaN in any column."""
+    raw = pd.read_csv(STORAGE / "la_land_values.csv")
+    assert not raw.isna().any().any()
+
+
+def test_csv_covers_all_four_countries():
+    """All four UK countries (E/W/S/NI) should appear."""
+    prefixes = LA_PRICES["code"].str[0].unique()
+    assert set(prefixes) == {"E", "W", "S", "N"}
+
+
+def test_house_prices_within_plausible_range():
+    """Avg house prices should be between £50k and £2m per LA."""
+    for _, row in LA_PRICES.iterrows():
+        assert 50_000 <= row["avg_house_price"] <= 2_000_000, (
+            f"{row['name']}: avg_house_price £{row['avg_house_price']:,} "
+            "outside plausible range"
+        )
+
+
+def test_csv_households_within_plausible_range():
+    """Smallest UK billing authority (Isles of Scilly) has ~1,100
+    households; largest (Birmingham) has ~450,000. The CSV `households`
+    column is retained as a regression fixture for the IoS fallback leak
+    even though the calibration target uses Census counts.
+    """
+    raw = pd.read_csv(STORAGE / "la_land_values.csv")
+    out_of_range = raw[~raw["households"].between(500, 500_000)]
+    assert out_of_range.empty, (
+        "CSV households out of plausible [500, 500_000] range: "
+        f"{out_of_range[['code', 'name', 'households']].to_dict('records')}"
+    )
+
+
+def test_isles_of_scilly_households_are_thousands_not_millions():
+    """Explicit regression for the IoS fallback leak (was 2,492,115)."""
+    raw = pd.read_csv(STORAGE / "la_land_values.csv")
+    ios = raw[raw["code"] == "E06000053"]
+    assert len(ios) == 1
+    hh = int(ios["households"].iloc[0])
+    assert 500 <= hh <= 5_000, (
+        f"Isles of Scilly households = {hh:,}; ONS mid-2023 estimate is ~1,115"
+    )
+
+
+# ── Target value constraints ─────────────────────────────────────────
+
+
+def test_targets_match_observed_product():
+    """Every target equals avg_price × ownership_share × n_households exactly.
+
+    No national-total apportionment, no rescaling: just the directly
+    observed product, identical in shape to the rent target.
+    """
+    prices = LA_PRICES.set_index("code")["avg_house_price"]
+    tenure = load_tenure_data().set_index("la_code")
+    households = load_household_counts().set_index("la_code")["households"]
+
+    for code, target in LA_TARGETS.items():
+        if code not in tenure.index or code not in households.index:
+            continue
+        ownership = (
+            tenure.loc[code, "owned_outright_pct"]
+            + tenure.loc[code, "owned_mortgage_pct"]
+        ) / 100
+        expected = prices.loc[code] * ownership * households.loc[code]
+        assert abs(target - expected) < 1e-3, (
+            f"{code}: target {target:,.2f} != expected {expected:,.2f}"
+        )
+
+
+def test_all_targets_positive():
+    """Every per-LA target should be positive."""
+    assert all(value > 0 for value in LA_TARGETS.values())
+
+
+def test_explicit_targets_cover_english_las():
+    """Direct-formula targets are produced for LAs with EHS tenure data
+    (England). Wales, Scotland and Northern Ireland LAs are handled by
+    the national-share fallback in loss.py — same as the existing
+    tenure target, by construction."""
+    prefixes = {code[0] for code in LA_TARGETS}
+    assert prefixes == {"E"}, (
+        f"Expected English-only targets from EHS data, got {sorted(prefixes)}"
+    )
+
+
+def test_kensington_and_chelsea_above_blackpool():
+    """K&C aggregate main-residence-value target should exceed Blackpool's."""
+    name_to_code = dict(zip(LA_PRICES["name"], LA_PRICES["code"]))
+    kc = LA_TARGETS[name_to_code["Kensington and Chelsea"]]
+    bp = LA_TARGETS[name_to_code["Blackpool"]]
+    assert kc > bp, (
+        f"K&C target (£{kc / 1e9:.1f}bn) should exceed Blackpool (£{bp / 1e9:.1f}bn)"
+    )
+
+
+def test_london_total_exceeds_north_east():
+    """Sum of London LA targets should exceed sum of North-East LA targets."""
+    london_codes = [c for c in LA_TARGETS if c.startswith("E09")]
+    ne_prefixes = {
+        "E06000001",
+        "E06000002",
+        "E06000003",
+        "E06000004",
+        "E06000005",
+        "E06000047",
+        "E08000021",
+        "E08000022",
+        "E08000023",
+        "E08000024",
+        "E08000037",
+        "E06000057",
+    }
+    ne_codes = [c for c in LA_TARGETS if c in ne_prefixes]
+    london_total = sum(LA_TARGETS[c] for c in london_codes)
+    ne_total = sum(LA_TARGETS[c] for c in ne_codes)
+    assert london_total > ne_total * 3, (
+        f"London total (£{london_total / 1e9:.0f}bn) should exceed "
+        f"NE total (£{ne_total / 1e9:.0f}bn) by at least 3x"
+    )
+
+
+# ── Target registry integration ──────────────────────────────────────
+
+
+def test_get_targets_returns_targets_for_covered_las():
+    """get_targets() returns one Target per LA with all inputs available."""
+    targets = get_targets()
+    assert len(targets) == len(LA_TARGETS)
+    assert {t.geo_code for t in targets} == set(LA_TARGETS)
+
+
+def test_target_names_follow_code_pattern():
+    """Target names should follow the housing/main_residence_value/{code} pattern."""
+    for t in get_targets():
+        assert t.name.startswith("housing/main_residence_value/")
+        assert t.name.removeprefix("housing/main_residence_value/") == t.geo_code
+
+
+def test_targets_declare_local_authority_geographic_level():
+    """All LA targets should be tagged with GeographicLevel.LOCAL_AUTHORITY."""
+    for t in get_targets():
+        assert t.geographic_level == GeographicLevel.LOCAL_AUTHORITY
+
+
+def test_targets_declare_hmlr_source():
+    """LA property-value targets are sourced from HMLR UK HPI."""
+    for t in get_targets():
+        assert t.source == "hmlr"
+
+
+def test_targets_have_calibration_year_values():
+    """LA targets should carry values for the supported calibration years."""
+    for t in get_targets():
+        assert {2024, 2025, 2026} <= set(t.values)
+
+
+def test_target_registry_includes_la_targets():
+    """LA property-value targets should appear in the global registry."""
+    from policyengine_uk_data.targets import get_all_targets
+
+    targets = get_all_targets(
+        year=2024, geographic_level=GeographicLevel.LOCAL_AUTHORITY
+    )
+    la_property = [
+        t for t in targets if t.name.startswith("housing/main_residence_value/")
+    ]
+    assert len(la_property) == len(LA_TARGETS), (
+        f"Expected {len(LA_TARGETS)} LA property-value targets, got {len(la_property)}"
+    )
diff --git a/policyengine_uk_data/tests/test_la_loss_land_value.py b/policyengine_uk_data/tests/test_la_loss_land_value.py
new file mode 100644
index 000000000..48bcfcb68
--- /dev/null
+++ b/policyengine_uk_data/tests/test_la_loss_land_value.py
@@ -0,0 +1,254 @@
+"""Tests for the LA-level main-residence-value column wired into the
+local-authority calibration loss matrix.
+
+Two layers:
+
+1. Light-weight checks against the per-LA target dict from la_land.py —
+   these run without a Microsimulation and exercise the ordering /
+   shape properties the loss-matrix code relies on.
+2. Full ``create_local_authority_target_matrix`` build, gated on the
+   enhanced FRS fixture so CI environments without the dataset skip
+   gracefully.
+"""
+
+import numpy as np
+import pandas as pd
+
+from policyengine_uk_data.storage import STORAGE_FOLDER
+from policyengine_uk_data.targets.sources.la_land import (
+    _compute_la_targets,
+    load_la_avg_prices,
+)
+from policyengine_uk_data.targets.sources.local_la_extras import (
+    load_household_counts,
+    load_tenure_data,
+)
+
+
+LA_CODES = pd.read_csv(STORAGE_FOLDER / "local_authorities_2021.csv")
+LA_TARGETS = _compute_la_targets()
+
+
+# ── Layer 1: per-LA targets line up with the LA code ordering ────────
+
+
+def test_explicit_targets_cover_english_las():
+    """Direct-formula targets are produced for LAs with EHS tenure data
+    (England). Other UK countries fall through to the national-share
+    fallback in loss.py — same as the existing tenure target."""
+    prefixes = {code[0] for code in LA_TARGETS}
+    assert prefixes == {"E"}
+
+
+def test_target_vector_in_la_codes_order_is_finite_positive_where_present():
+    """Reindexing by la_codes order yields a clean float vector for
+    LAs with a target; LAs missing inputs become NaN (later filled by
+    the national-share fallback inside loss.py)."""
+    vec = LA_CODES["code"].map(LA_TARGETS).values
+    finite = vec[~np.isnan(vec.astype(float))]
+    assert len(vec) == 360
+    assert (finite > 0).all()
+
+
+def test_targets_match_observed_product_inline():
+    """Per-LA target equals avg_price × ownership_share × n_households —
+    the same shape as private rent's ``median_rent × renter_pct × n_hh``.
+    """
+    prices = load_la_avg_prices().set_index("code")["avg_house_price"]
+    tenure = load_tenure_data().set_index("la_code")
+    households = load_household_counts().set_index("la_code")["households"]
+
+    for code, target in LA_TARGETS.items():
+        if code not in tenure.index or code not in households.index:
+            continue
+        ownership = (
+            tenure.loc[code, "owned_outright_pct"]
+            + tenure.loc[code, "owned_mortgage_pct"]
+        ) / 100
+        expected = prices.loc[code] * ownership * households.loc[code]
+        assert abs(target - expected) < 1e-3
+
+
+# ── Layer 2: full LA loss matrix build ───────────────────────────────
+
+
+def test_la_loss_matrix_includes_main_residence_value(enhanced_frs):
+    """The LA target matrix must expose housing/main_residence_value in
+    both matrix (per-household) and y (per-LA) so the calibrator can
+    train on it."""
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    matrix, y, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    assert "housing/main_residence_value" in matrix.columns
+    assert "housing/main_residence_value" in y.columns
+
+
+def test_la_loss_y_vector_length_360(enhanced_frs):
+    """y has one entry per LA and matches local_authorities_2021.csv ordering
+    by length."""
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    _, y, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    assert len(y) == 360
+    assert len(y["housing/main_residence_value"]) == 360
+
+
+def test_la_loss_y_matches_observed_product_for_covered_las(enhanced_frs):
+    """For LAs with all inputs present, y equals avg_price × ownership × n_households.
+
+    LAs missing inputs use the national-share fallback (covered in
+    test_la_loss_y_all_positive)."""
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    _, y, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    expected_by_code = LA_TARGETS
+    for i, code in enumerate(LA_CODES["code"].values):
+        if code not in expected_by_code:
+            continue  # fallback path
+        actual = y["housing/main_residence_value"].iloc[i]
+        expected = expected_by_code[code]
+        assert abs(actual - expected) < 1e-3, (
+            f"{code}: y {actual:,.2f} != expected {expected:,.2f}"
+        )
+
+
+def test_la_loss_y_all_positive(enhanced_frs):
+    """No LA should have a non-positive main-residence-value target."""
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    _, y, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    assert (y["housing/main_residence_value"] > 0).all()
+
+
+def test_la_loss_matrix_column_matches_main_residence_value(enhanced_frs):
+    """matrix['housing/main_residence_value'] should equal the per-household
+    main_residence_value pulled from policyengine-uk for the calibration year."""
+    from policyengine_uk import Microsimulation
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    matrix, _, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    sim = Microsimulation(dataset=enhanced_frs)
+    sim.default_calculation_period = enhanced_frs.time_period
+    expected = sim.calculate("main_residence_value").values
+
+    np.testing.assert_array_equal(
+        matrix["housing/main_residence_value"].values, expected
+    )
+
+
+# ── Layer 2b: calibration well-formedness ─────────────────────────────
+
+
+def test_la_loss_y_has_no_nan(enhanced_frs):
+    """Every LA must have a numeric target. NaN entries would propagate
+    through the optimiser and fail calibration silently."""
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    _, y, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    assert not y["housing/main_residence_value"].isna().any()
+
+
+def test_la_loss_fallback_applied_to_non_english_las(enhanced_frs):
+    """Wales / Scotland / NI LAs use the national-share fallback because
+    EHS only covers England. Their y entry must still be positive
+    (so the optimiser has a target to fit) and must NOT equal the
+    direct-formula value (which is undefined when ownership share
+    is missing)."""
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    _, y, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    fallback_codes = [c for c in LA_CODES["code"] if not c.startswith("E")]
+    fallback_indices = [
+        i for i, c in enumerate(LA_CODES["code"].values) if c in fallback_codes
+    ]
+    fallback_values = y["housing/main_residence_value"].iloc[fallback_indices]
+
+    assert (fallback_values > 0).all()
+    assert fallback_values.notna().all()
+
+
+def test_la_loss_matrix_column_carries_calibration_signal(enhanced_frs):
+    """matrix['housing/main_residence_value'] must vary across households —
+    a constant column gives the optimiser no signal to differentiate
+    LAs and the new target would be inert."""
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    matrix, _, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    column = matrix["housing/main_residence_value"].values
+    assert column.var() > 0
+    assert (column > 0).any(), "no households with positive main_residence_value"
+
+
+def test_la_loss_english_target_total_within_reach_of_initial_weights(enhanced_frs):
+    """Sum of English LA targets should be in the same order of magnitude
+    as the implied initial English main-residence-value — so the
+    optimiser has a chance of hitting them via reweighting rather than
+    requiring weights to inflate by 100x."""
+    from policyengine_uk import Microsimulation
+    from policyengine_uk_data.datasets.local_areas.local_authorities.loss import (
+        create_local_authority_target_matrix,
+    )
+
+    _, y, _ = create_local_authority_target_matrix(
+        enhanced_frs, time_period=enhanced_frs.time_period
+    )
+
+    sim = Microsimulation(dataset=enhanced_frs)
+    original_weights = sim.calculate("household_weight", 2025).values
+    main_res = sim.calculate("main_residence_value", enhanced_frs.time_period).values
+    country = sim.calculate("country", enhanced_frs.time_period).values
+
+    england_mask = country == "ENGLAND"
+    england_initial = (original_weights[england_mask] * main_res[england_mask]).sum()
+
+    english_indices = [
+        i for i, c in enumerate(LA_CODES["code"].values) if c.startswith("E")
+    ]
+    english_targets = y["housing/main_residence_value"].iloc[english_indices].sum()
+
+    ratio = english_targets / england_initial
+    assert 0.5 < ratio < 3.0, (
+        f"English LA target sum (£{english_targets / 1e9:.0f}bn) / "
+        f"initial English main-residence-value (£{england_initial / 1e9:.0f}bn) "
+        f"= {ratio:.2f}; calibration target may be hard to reach"
+    )